Consolidate the database
We generated the database with the new requested variables. Must note that this database already filtererd minors at admission but people that did not reported a valid age (remember differences with Stata).
Show code
invisible("84,936 x 254 # 2020-10-25")
invisible("84,944 x 277 # 2020-12-20")
prueba<-
CONS_C1_df_dup_SEP_2020 %>%
dplyr::group_by(hash_key) %>%
dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>%
dplyr::ungroup() %>%
dplyr::filter(edad_al_ing>=18| is.na(edad_al_ing)) %>% #Sólo así llegamos a 109,642 casos, igual que en STATA
dplyr::group_by(hash_key) %>%
dplyr::mutate(dup2=row_number()) %>%
dplyr::mutate(duplicates_filtered2=n()) %>%
dplyr::mutate(max_cum_dias_trat_sin_na=max(cum_dias_trat_sin_na, na.rm=T)) %>%
dplyr::mutate(max_cum_diff_bet_treat=max(cum_diff_bet_treat, na.rm=T)) %>%
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#Formatear días de tratamiento y diferencia en días excliyendo a los casos a los que le borramos un tratamiento menor a 18.
#local vars `" "diff_bet_treat_" "dias_treat_imp_sin_na_" "cum_diff_bet_treat_" "cum_dias_trat_sin_na_" "mean_cum_dias_trat_sin_na_" "mean_cum_diff_bet_treat_" "'
dplyr::ungroup() %>%
dplyr::filter(dup2==1) %>% #84,936 x 254 # 2020-10-25,
dplyr::mutate(cum_dias_trat_sin_na_1= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_2,TRUE~cum_dias_trat_sin_na_1)) %>%
dplyr::mutate(cum_dias_trat_sin_na_2= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_3,TRUE~cum_dias_trat_sin_na_2)) %>%
dplyr::mutate(cum_dias_trat_sin_na_3= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_4,TRUE~cum_dias_trat_sin_na_3)) %>%
dplyr::mutate(cum_dias_trat_sin_na_4= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_5,TRUE~cum_dias_trat_sin_na_4)) %>%
dplyr::mutate(cum_dias_trat_sin_na_5= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_6,TRUE~cum_dias_trat_sin_na_5)) %>%
dplyr::mutate(cum_dias_trat_sin_na_6= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_7,TRUE~cum_dias_trat_sin_na_6)) %>%
dplyr::mutate(cum_dias_trat_sin_na_7= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_8,TRUE~cum_dias_trat_sin_na_7)) %>%
dplyr::mutate(cum_dias_trat_sin_na_8= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_9,TRUE~cum_dias_trat_sin_na_8)) %>%
dplyr::mutate(cum_dias_trat_sin_na_9= dplyr::case_when(menor_edad>0~cum_dias_trat_sin_na_10,TRUE~cum_dias_trat_sin_na_9)) %>%
dplyr::mutate(cum_dias_trat_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~cum_dias_trat_sin_na_10)) %>%
dplyr::mutate(cum_diff_bet_treat_1= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_2,TRUE~cum_diff_bet_treat_1)) %>%
dplyr::mutate(cum_diff_bet_treat_2= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_3,TRUE~cum_diff_bet_treat_2)) %>%
dplyr::mutate(cum_diff_bet_treat_3= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_4,TRUE~cum_diff_bet_treat_3)) %>%
dplyr::mutate(cum_diff_bet_treat_4= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_5,TRUE~cum_diff_bet_treat_4)) %>%
dplyr::mutate(cum_diff_bet_treat_5= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_6,TRUE~cum_diff_bet_treat_5)) %>%
dplyr::mutate(cum_diff_bet_treat_6= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_7,TRUE~cum_diff_bet_treat_6)) %>%
dplyr::mutate(cum_diff_bet_treat_7= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_8,TRUE~cum_diff_bet_treat_7)) %>%
dplyr::mutate(cum_diff_bet_treat_8= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_9,TRUE~cum_diff_bet_treat_8)) %>%
dplyr::mutate(cum_diff_bet_treat_9= dplyr::case_when(menor_edad>0~cum_diff_bet_treat_10,TRUE~cum_diff_bet_treat_9)) %>%
dplyr::mutate(cum_diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~cum_diff_bet_treat_10)) %>%
dplyr::mutate(tipo_de_plan_2_1= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_2),TRUE~as.character(tipo_de_plan_2_1))) %>%
dplyr::mutate(tipo_de_plan_2_2= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_3),TRUE~as.character(tipo_de_plan_2_2))) %>%
dplyr::mutate(tipo_de_plan_2_3= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_4),TRUE~as.character(tipo_de_plan_2_3))) %>%
dplyr::mutate(tipo_de_plan_2_4= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_5),TRUE~as.character(tipo_de_plan_2_4))) %>%
dplyr::mutate(tipo_de_plan_2_5= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_6),TRUE~as.character(tipo_de_plan_2_5))) %>%
dplyr::mutate(tipo_de_plan_2_6= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_7),TRUE~as.character(tipo_de_plan_2_6))) %>%
dplyr::mutate(tipo_de_plan_2_7= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_8),TRUE~as.character(tipo_de_plan_2_7))) %>%
dplyr::mutate(tipo_de_plan_2_8= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_9),TRUE~as.character(tipo_de_plan_2_8))) %>%
dplyr::mutate(tipo_de_plan_2_9= dplyr::case_when(menor_edad>0~as.character(tipo_de_plan_2_10),TRUE~as.character(tipo_de_plan_2_9))) %>%
dplyr::mutate(tipo_de_plan_2_10= dplyr::case_when(menor_edad>0~NA_character_,TRUE~as.character(tipo_de_plan_2_10))) %>%
dplyr::mutate(motivodeegreso_mod_imp_1= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_2),TRUE~as.character(motivodeegreso_mod_imp_1))) %>%
dplyr::mutate(motivodeegreso_mod_imp_2= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_3),TRUE~as.character(motivodeegreso_mod_imp_2))) %>%
dplyr::mutate(motivodeegreso_mod_imp_3= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_4),TRUE~as.character(motivodeegreso_mod_imp_3))) %>%
dplyr::mutate(motivodeegreso_mod_imp_4= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_5),TRUE~as.character(motivodeegreso_mod_imp_4))) %>%
dplyr::mutate(motivodeegreso_mod_imp_5= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_6),TRUE~as.character(motivodeegreso_mod_imp_5))) %>%
dplyr::mutate(motivodeegreso_mod_imp_6= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_7),TRUE~as.character(motivodeegreso_mod_imp_6))) %>%
dplyr::mutate(motivodeegreso_mod_imp_7= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_8),TRUE~as.character(motivodeegreso_mod_imp_7))) %>%
dplyr::mutate(motivodeegreso_mod_imp_8= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_9),TRUE~as.character(motivodeegreso_mod_imp_8))) %>%
dplyr::mutate(motivodeegreso_mod_imp_9= dplyr::case_when(menor_edad>0~as.character(motivodeegreso_mod_imp_10),TRUE~as.character(motivodeegreso_mod_imp_9))) %>%
dplyr::mutate(motivodeegreso_mod_imp_10= dplyr::case_when(menor_edad>0~NA_character_,TRUE~as.character(motivodeegreso_mod_imp_10))) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_1= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_2,TRUE~mean_cum_dias_trat_sin_na_1)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_2= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_3,TRUE~mean_cum_dias_trat_sin_na_2)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_3= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_4,TRUE~mean_cum_dias_trat_sin_na_3)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_4= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_5,TRUE~mean_cum_dias_trat_sin_na_4)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_5= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_6,TRUE~mean_cum_dias_trat_sin_na_5)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_6= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_7,TRUE~mean_cum_dias_trat_sin_na_6)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_7= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_8,TRUE~mean_cum_dias_trat_sin_na_7)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_8= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_9,TRUE~mean_cum_dias_trat_sin_na_8)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_9= dplyr::case_when(menor_edad>0~mean_cum_dias_trat_sin_na_10,TRUE~mean_cum_dias_trat_sin_na_9)) %>%
dplyr::mutate(mean_cum_dias_trat_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~mean_cum_dias_trat_sin_na_10)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_1= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_2,TRUE~mean_cum_diff_bet_treat_1)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_2= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_3,TRUE~mean_cum_diff_bet_treat_2)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_3= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_4,TRUE~mean_cum_diff_bet_treat_3)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_4= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_5,TRUE~mean_cum_diff_bet_treat_4)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_5= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_6,TRUE~mean_cum_diff_bet_treat_5)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_6= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_7,TRUE~mean_cum_diff_bet_treat_6)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_7= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_8,TRUE~mean_cum_diff_bet_treat_7)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_8= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_9,TRUE~mean_cum_diff_bet_treat_8)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_9= dplyr::case_when(menor_edad>0~mean_cum_diff_bet_treat_10,TRUE~mean_cum_diff_bet_treat_9)) %>%
dplyr::mutate(mean_cum_diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~mean_cum_diff_bet_treat_10)) %>%
dplyr::mutate(diff_bet_treat_1= dplyr::case_when(menor_edad>0~diff_bet_treat_2,TRUE~diff_bet_treat_1)) %>%
dplyr::mutate(diff_bet_treat_2= dplyr::case_when(menor_edad>0~diff_bet_treat_3,TRUE~diff_bet_treat_2)) %>%
dplyr::mutate(diff_bet_treat_3= dplyr::case_when(menor_edad>0~diff_bet_treat_4,TRUE~diff_bet_treat_3)) %>%
dplyr::mutate(diff_bet_treat_4= dplyr::case_when(menor_edad>0~diff_bet_treat_5,TRUE~diff_bet_treat_4)) %>%
dplyr::mutate(diff_bet_treat_5= dplyr::case_when(menor_edad>0~diff_bet_treat_6,TRUE~diff_bet_treat_5)) %>%
dplyr::mutate(diff_bet_treat_6= dplyr::case_when(menor_edad>0~diff_bet_treat_7,TRUE~diff_bet_treat_6)) %>%
dplyr::mutate(diff_bet_treat_7= dplyr::case_when(menor_edad>0~diff_bet_treat_8,TRUE~diff_bet_treat_7)) %>%
dplyr::mutate(diff_bet_treat_8= dplyr::case_when(menor_edad>0~diff_bet_treat_9,TRUE~diff_bet_treat_8)) %>%
dplyr::mutate(diff_bet_treat_9= dplyr::case_when(menor_edad>0~diff_bet_treat_10,TRUE~diff_bet_treat_9)) %>%
dplyr::mutate(diff_bet_treat_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~diff_bet_treat_10)) %>%
dplyr::mutate(dias_treat_imp_sin_na_1= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_2,TRUE~dias_treat_imp_sin_na_1)) %>%
dplyr::mutate(dias_treat_imp_sin_na_2= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_3,TRUE~dias_treat_imp_sin_na_2)) %>%
dplyr::mutate(dias_treat_imp_sin_na_3= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_4,TRUE~dias_treat_imp_sin_na_3)) %>%
dplyr::mutate(dias_treat_imp_sin_na_4= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_5,TRUE~dias_treat_imp_sin_na_4)) %>%
dplyr::mutate(dias_treat_imp_sin_na_5= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_6,TRUE~dias_treat_imp_sin_na_5)) %>%
dplyr::mutate(dias_treat_imp_sin_na_6= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_7,TRUE~dias_treat_imp_sin_na_6)) %>%
dplyr::mutate(dias_treat_imp_sin_na_7= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_8,TRUE~dias_treat_imp_sin_na_7)) %>%
dplyr::mutate(dias_treat_imp_sin_na_8= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_9,TRUE~dias_treat_imp_sin_na_8)) %>%
dplyr::mutate(dias_treat_imp_sin_na_9= dplyr::case_when(menor_edad>0~dias_treat_imp_sin_na_10,TRUE~dias_treat_imp_sin_na_9)) %>%
dplyr::mutate(dias_treat_imp_sin_na_10= dplyr::case_when(menor_edad>0~NA_real_,TRUE~dias_treat_imp_sin_na_10)) %>%
# diff y dias treat
dplyr::mutate(dias_treat_imp_sin_na_four = rowMeans(dplyr::select(., dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(diff_bet_treat_four = rowMeans(dplyr::select(., diff_bet_treat_4, diff_bet_treat_5, diff_bet_treat_6, diff_bet_treat_7, diff_bet_treat_8, diff_bet_treat_9, diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# cum
dplyr::mutate(cum_dias_trat_sin_na_four = rowMeans(dplyr::select(., cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(cum_diff_bet_treat_four = rowMeans(dplyr::select(., cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# mean cum
dplyr::mutate(mean_cum_dias_trat_sin_na_four = rowMeans(dplyr::select(., mean_cum_dias_trat_sin_na_4, mean_cum_dias_trat_sin_na_5, mean_cum_dias_trat_sin_na_6, mean_cum_dias_trat_sin_na_7, mean_cum_dias_trat_sin_na_8, mean_cum_dias_trat_sin_na_9, mean_cum_dias_trat_sin_na_10), na.rm=T)) %>% #,na.rm=F
dplyr::mutate(mean_cum_diff_bet_treat_four = rowMeans(dplyr::select(., mean_cum_diff_bet_treat_4, mean_cum_diff_bet_treat_5, mean_cum_diff_bet_treat_6, mean_cum_diff_bet_treat_7, mean_cum_diff_bet_treat_8, mean_cum_diff_bet_treat_9, mean_cum_diff_bet_treat_10), na.rm=T)) %>% #,na.rm=F
# tipo de plana
dplyr::mutate(tipo_de_plan_2_mod=dplyr::case_when(grepl("PAB",tipo_de_plan_2)~"PAB",
grepl("PAI",tipo_de_plan_2)~"PAI",
grepl("PR",tipo_de_plan_2)~"PR",
TRUE~NA_character_)) %>%
dplyr::mutate(tipo_de_plan_2_mod=factor(tipo_de_plan_2_mod)) %>%
dplyr::mutate(estatus_ocupacional= dplyr::case_when(!is.na(cat_ocupacional)&!is.na(estatus_ocupacional)~"Empleado",
TRUE~as.character(estatus_ocupacional)))%>%
dplyr::mutate(estatus_ocupacional= as.factor(estatus_ocupacional))%>%
dplyr::mutate(cnt_mod_cie_10_dg_cons_sus_or= dplyr::case_when(as.character(dg_trs_cons_sus_or)=="Drug dependence"~dg_total_cie_10+1,
TRUE~dg_total_cie_10))%>%
dplyr::mutate(freq_cons_sus_prin= dplyr::case_when(as.character(freq_cons_sus_prin)=="Did not use"~"Less than 1 day a week",
TRUE~as.character(freq_cons_sus_prin)))%>%
dplyr::mutate(freq_cons_sus_prin= as.factor(freq_cons_sus_prin)) %>%
#edad más 65
dplyr::mutate(mas_65= dplyr::case_when(edad_al_ing>65~1,
TRUE~0))%>%
dplyr::mutate(mas_65= as.factor(mas_65)) %>%
dplyr::mutate(comorbidity_icd_10=dplyr::case_when(dg_total_cie_10>=2~ "Two or more",
dg_total_cie_10==1~ "One",
as.character(dg_cie_10_rec)=="Diagnosis unknown (under study)"~"Diagnosis unknown (under study)",
as.character(dg_cie_10_rec)=="Without psychiatric comorbidity"~"Without psychiatric comorbidity")) %>%
dplyr::mutate(comorbidity_icd_10=as.factor(comorbidity_icd_10)) %>%
# dplyr::select(-menor_edad) %>%
dplyr::mutate(no_group=1) %>%
dplyr::mutate(had_readm=dplyr::case_when(duplicates_filtered2>1~1,
TRUE~0)) %>%
dplyr::mutate(n_treats=factor(dplyr::case_when(duplicates_filtered2>3~"04 or more",
duplicates_filtered2==3~"03",
duplicates_filtered2==2~"02",
duplicates_filtered2==1~"01")))
Updated in 2021 June & August
We only left users in treatments in General Population (PG) and
missing values and discarded under 18 years (n= 84,944). Additionally,
we recategorized the variable ‘Living with’
(con_quien_vive).
Show code
#janitor::tabyl(CONS_C1_df_dup_SEP_2020$otros_probl_at_sm_or)
#round(prop.table(table(CONS_C1_df_dup_SEP_2020$otros_probl_at_sm_or)),3)
#With With Other Alone With With
#relatives couple and children children only couple only
#38105 21806 7275 8026 3191 6540
library(readr)
prueba2<-
prueba %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#AUG 2021:
dplyr::mutate(con_quien_vive_joel=dplyr::case_when(
grepl("Solo$",con_quien_vive, ignore.case=T)~"Alone",
grepl("Con abuelos",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con hermanos",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con la madre \\(sola\\)",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("Con otro pariente",con_quien_vive, ignore.case=T)~"Others",
grepl("con hijos y padres o familia",con_quien_vive, ignore.case=T)~"Family of origin",
grepl("con la pareja y padres o familia de origen",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("con padres o familia de origen",con_quien_vive, ignore.case=T)~"Family of origin",
#2021-10-01
grepl("Únicamente con hijos",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Únicamente con pareja",con_quien_vive, ignore.case=T)~"With couple/children",
#2021-10-01
grepl("Con la Pareja, Hijos y Padres o Familia de Origen",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Hijos y Padres o Familia de Origen",con_quien_vive, ignore.case=T)~"Family of origin",
#2021-10-01
grepl("Únicamente con la pareja e hijos",con_quien_vive, ignore.case=T)~"With couple/children",
grepl("Con amigos",con_quien_vive, ignore.case=T)~"Others",
grepl("Con otro NO pariente",con_quien_vive, ignore.case=T)~"Others",
grepl("*Otros$",con_quien_vive, ignore.case=T)~"Others")) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#janitor::tabyl(con_quien_vive, con_quien_vive_rec)
dplyr::filter(!grepl("M-",tipo_de_plan_2)) %>%
#janitor::tabyl(embarazo)
#No Si <NA>
#76081 496 89
#prueba2 %>% janitor::tabyl(embarazo,tiene_menores_de_edad_a_cargo)
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(numero_de_hijos_mod_joel=dplyr::case_when(
grepl("Si$",embarazo, ignore.case=T)~as.integer(numero_de_hijos_mod+1),
T~as.integer(numero_de_hijos_mod)))%>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::filter(edad_al_ing_grupos=="18-29") %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(dom_violence=factor(dplyr::case_when(
grepl("Violencia Intrafamiliar$",otros_probl_at_sm_or, ignore.case=T)~1,
is.na(otros_probl_at_sm_or)~NA_real_,
T~0),levels=c(0,1),labels=c("No domestic violence","Domestic violence"))) %>%
dplyr::mutate(sex_abuse=factor(dplyr::case_when(
grepl("Abuso Sexual",otros_probl_at_sm_or, ignore.case=T)~1,
is.na(otros_probl_at_sm_or)~NA_real_,
T~0),levels=c(0,1),labels=c("No sexual abuse","Sexual abuse"))) %>%
dplyr::filter(!grepl("Others",con_quien_vive_joel)) %>%
#5 de agosto, paso de 26,236 a 23,979, descartando 2,257 casos que tienen "otros".
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#TENENCIA VIVIENDA
#Allegado, Arrienda, Cedida, Ocupación Irregular, Otros, Paga dividendo, Propia
#vive transitoriamente en casa ajena
dplyr::mutate(tenencia_de_la_vivienda_mod=
factor(dplyr::case_when(tenencia_de_la_vivienda_mod=="Allegado"~"Stays temporarily with a relative",
tenencia_de_la_vivienda_mod=="Arrienda"~"Renting",
tenencia_de_la_vivienda_mod=="Cedida"~"Owner/Transferred dwellings/Pays Dividends",
tenencia_de_la_vivienda_mod=="Ocupación Irregular"~"Illegal Settlement",
tenencia_de_la_vivienda_mod=="Otros"~"Others",
tenencia_de_la_vivienda_mod=="Paga dividendo"~"Owner/Transferred dwellings/Pays Dividends",
tenencia_de_la_vivienda_mod=="Propia"~"Owner/Transferred dwellings/Pays Dividends",
T~NA_character_))) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#DAR ESTRUCTURA ORDINAL#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
dplyr::mutate(escolaridad_rec=parse_factor(as.character(escolaridad_rec),levels=c('3-Completed primary school or less', '2-Completed high school or less', '1-More than high school'), ordered =T,trim_ws=T,include_na =F, locale=locale(encoding = "Latin1"))) %>%
dplyr::mutate(freq_cons_sus_prin=parse_factor(as.character(freq_cons_sus_prin),levels=c('Less than 1 day a week','2 to 3 days a week','4 to 6 days a week','1 day a week or more','Daily'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(compromiso_biopsicosocial=parse_factor(as.character(compromiso_biopsicosocial),levels=c('1-Mild', '2-Moderate','3-Severe'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(comorbidity_icd_10=parse_factor(as.character(comorbidity_icd_10),levels=c('Without psychiatric comorbidity', 'Diagnosis unknown (under study)','One','Two or more'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
dplyr::mutate(cnt_mod_cie_10_or=parse_factor(as.character(cnt_mod_cie_10_or),levels=c('0', '1','2','3'), ordered =T,trim_ws=F,include_na =F)) %>% #, locale=locale(encoding = "Latin1")
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#dplyr::mutate(edad_al_ing_grupos=if_else(edad_al_ing_grupos=='18-29','<18-29',as.character(edad_al_ing_grupos),NA_character_)) %>%
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#FRECUENCIA CONSUMO: Regroup 1 día a la semana y No usó en el último mes
dplyr::mutate(freq_cons_sus_prin=if_else(freq_cons_sus_prin=='Did not use','Less than 1 day a week',as.character(freq_cons_sus_prin),NA_character_))
#"escolaridad_rec", "freq_cons_sus_prin", "num_otras_sus_mod", "numero_de_hijos_mod_joel", "cnt_mod_cie_10_or", "comorbidity_icd_10", "compromiso_biopsicosocial"
#numero_de_hijos_mod #Número de Hijos (Valor Max.)/Number of Children (Max. Value)
#hijos_trat_res #Tiene Hijos en Ingreso a Tratamiento Residencial del Último Registro/Have Children in Residential Treatment of the Last Entry
#prueba2 %>% janitor::tabyl(numero_de_hijos_mod,embarazo)
#prueba2 %>% janitor::tabyl(numero_de_hijos_mod,hijos_trat_res) #me parece que está bien, no hay contradicciones
#prueba2 %>% janitor::tabyl(con_quien_vive,con_quien_vive_joel)
#prueba2 %>% janitor::tabyl(con_quien_vive,con_quien_vive_joel) %>% copiar_nombres()
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
We ended having 23,979 users.
In the data base for 2021, only includes general population or mixed-gender programs at baseline. The variable whether the respondent had children at admission to a residential treatment had many missing values (16%). In the cases that is feasible to have children, it shows 3% of missing values.
Must note that there are patients that may respond that they have children in treatment and respond that they have more than a child. But there can be people that did not report children and having children in residential treatments.
- ‘Starting substance’ (
sus_ini_mod_mvv)= Most vulnerable value. - ‘Number of children’(
numero_de_hijos_mod_joel)= If a user was pregnant, we added one children into the count of the number of children, because of the sparsity of pregnancy status (5.22%). - ‘Primary or main substance’(
sus_principal_mod)= We used the primary substance at admission of the largest treatment for continuous treatments. Every substance was chosen from the largest treatment, excepting cases where the value was not available in the largest treatment. - ‘Tenure status of households’
(
tenencia_de_la_vivienda_mod)= Three categories were collapsed into one single condition: Owner (28.66%), with Transferred dwellings (3.76%) and Pays Dividend (1.79%). - ‘Educational Attainment’ (
escolaridad_rec)= We selected the most vulnerable category among continuous treatments for the same user. - ‘Consumption frequency of primary or main
substance’(
freq_cons_sus_prin)= Among the categories of the Frequency of Drug Consumption, we grouped Less than 1 day a week (3.16%) with Did not use in the last 30 days (1.80%)
Total
Show code
#browse hash_key duplicates_filtered2 cum_diff_bet_treat dup2_real diff_bet_treat if hash_key=="0737aacbb7efdd418f7a37ce3386ce5e"|hash_key=="07668f2d3e4f6beb7975e43ee96eac80"
#diff_bet_treat_five+ cum_dias_trat_sin_na_five+ cum_diff_bet_treat_five+
# mean_cum_dias_trat_sin_na_five+ mean_cum_diff_bet_treat_five,
attr(prueba$n_treats,"label") <- "No. of treatments with 18+ at admission between 2010 and 2019"
attr(prueba$comorbidity_icd_10,"label") <- "Comorbidity ICD-10 (with amount of different diagnosis)"
attr(prueba$dias_treat_imp_sin_na_four,"label") <- "Days of Treatment (Fourth or those that follow)"
attr(prueba$diff_bet_treat_four,"label") <- "Days of Difference Between Treatments (Fifth treatment or those that folow)"
attr(prueba$cum_dias_trat_sin_na_four,"label") <- "Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba$cum_diff_bet_treat_four,"label") <- "Cumulative Difference Between Treatments (Fifth or those that follow)" #hash_key
attr(prueba$mean_cum_dias_trat_sin_na_four,"label") <- "Average Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba$mean_cum_diff_bet_treat_four,"label") <- "Average Cumulative Difference Between Treatments (Fifth or those that follow)"
attr(prueba$cnt_mod_cie_10_dg_cons_sus_or,"label") <- "Total count of Psychiatric & Drug dependence Diagnostics"
attr(prueba$max_cum_diff_bet_treat,"label") <- "Max. Cumulative Difference Between Treatments"
attr(prueba$max_cum_dias_trat_sin_na,"label") <- "Max. Cumulative Days of Treatment"
attr(prueba$tipo_de_plan_2_mod,"label") <- "Type of Plan (Independently of the Program)"
attr(prueba$condicion_ocupacional_corr,"label") <- 'Occupational Status Corrected(f)'
attr(prueba$cat_ocupacional_corr,"label") <- 'Occupational Category Corrected(f)'
attr(prueba$compromiso_biopsicosocial,"label") <- 'Biopsychosocial Compromise'
attr(prueba$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
#attr(prueba$,"label")<-"Starting Substance"
attr(prueba$sus_principal_mod,"label")<-"Starting Substance"
attr(prueba$cum_dias_trat_sin_na_1,"label") <- "Cum. Days of Treatment (1st Treatment)"
attr(prueba$cum_dias_trat_sin_na_2,"label") <- "Cum. Days of Treatment (2nd Treatment)"
attr(prueba$cum_dias_trat_sin_na_3,"label") <- "Cum. Days of Treatment (3rd Treatment)"
attr(prueba$cum_dias_trat_sin_na_4,"label") <- "Cum. Days of Treatment (4th Treatment)"
attr(prueba$cum_dias_trat_sin_na_5,"label") <- "Cum. Days of Treatment (5th Treatment)"
attr(prueba$cum_dias_trat_sin_na_6,"label") <- "Cum. Days of Treatment (6th Treatment)"
attr(prueba$cum_dias_trat_sin_na_7,"label") <- "Cum. Days of Treatment (7th Treatment)"
attr(prueba$cum_dias_trat_sin_na_8,"label") <- "Cum. Days of Treatment (8th Treatment)"
attr(prueba$cum_dias_trat_sin_na_9,"label") <- "Cum. Days of Treatment (9th Treatment)"
attr(prueba$cum_dias_trat_sin_na_10,"label") <-"Cum. Days of Treatment (10th Treatment)"
attr(prueba$cum_diff_bet_treat_1,"label") <- "Cum. Diff Between Treatments (1st Treatment)"
attr(prueba$cum_diff_bet_treat_2,"label") <- "Cum. Diff Between Treatments (2nd Treatment)"
attr(prueba$cum_diff_bet_treat_3,"label") <- "Cum. Diff Between Treatments (3rd Treatment)"
attr(prueba$cum_diff_bet_treat_4,"label") <- "Cum. Diff Between Treatments (4th Treatment)"
attr(prueba$cum_diff_bet_treat_5,"label") <- "Cum. Diff Between Treatments (5th Treatment)"
attr(prueba$cum_diff_bet_treat_6,"label") <- "Cum. Diff Between Treatments (6th Treatment)"
attr(prueba$cum_diff_bet_treat_7,"label") <- "Cum. Diff Between Treatments (7th Treatment)"
attr(prueba$cum_diff_bet_treat_8,"label") <- "Cum. Diff Between Treatments (8th Treatment)"
attr(prueba$cum_diff_bet_treat_9,"label") <- "Cum. Diff Between Treatments (9th Treatment)"
attr(prueba$cum_diff_bet_treat_10,"label") <-"Cum. Diff Between Treatments (10th Treatment)"
attr(prueba2$n_treats,"label") <- "No. of treatments with 18+ at admission between 2010 and 2019"
attr(prueba2$comorbidity_icd_10,"label") <- "Comorbidity ICD-10 (with amount of different diagnosis)"
attr(prueba2$dias_treat_imp_sin_na_four,"label") <- "Days of Treatment (Fourth or those that follow)"
attr(prueba2$diff_bet_treat_four,"label") <- "Days of Difference Between Treatments (Fifth treatment or those that folow)"
attr(prueba2$cum_dias_trat_sin_na_four,"label") <- "Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba2$cum_diff_bet_treat_four,"label") <- "Cumulative Difference Between Treatments (Fifth or those that follow)" #hash_key
attr(prueba2$mean_cum_dias_trat_sin_na_four,"label") <- "Average Cumulative Days of Treatment (Fourth or those that follow)"
attr(prueba2$mean_cum_diff_bet_treat_four,"label") <- "Average Cumulative Difference Between Treatments (Fifth or those that follow)"
attr(prueba2$cnt_mod_cie_10_dg_cons_sus_or,"label") <- "Total count of Psychiatric & Drug dependence Diagnostics"
attr(prueba2$max_cum_diff_bet_treat,"label") <- "Max. Cumulative Difference Between Treatments"
attr(prueba2$max_cum_dias_trat_sin_na,"label") <- "Max. Cumulative Days of Treatment"
attr(prueba2$tipo_de_plan_2_mod,"label") <- "Type of Plan (Independently of the Program)"
attr(prueba2$condicion_ocupacional_corr,"label") <- 'Occupational Status Corrected(f)'
attr(prueba2$cat_ocupacional_corr,"label") <- 'Occupational Category Corrected(f)'
attr(prueba2$con_quien_vive_joel,"label") <- 'Whom you live with(cohabitation status) (Recoded) (f)'
attr(prueba2$compromiso_biopsicosocial,"label") <- 'Biopsychosocial Compromise'
attr(prueba2$tenencia_de_la_vivienda_mod,"label") <- 'Tenure status of households'
attr(prueba2$sus_ini_mod_mvv,"label")<-"Starting Substance"
attr(prueba2$cum_dias_trat_sin_na_1,"label") <- "Cum. Days of Treatment (1st Treatment)"
attr(prueba2$cum_dias_trat_sin_na_2,"label") <- "Cum. Days of Treatment (2nd Treatment)"
attr(prueba2$cum_dias_trat_sin_na_3,"label") <- "Cum. Days of Treatment (3rd Treatment)"
attr(prueba2$cum_dias_trat_sin_na_4,"label") <- "Cum. Days of Treatment (4th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_5,"label") <- "Cum. Days of Treatment (5th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_6,"label") <- "Cum. Days of Treatment (6th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_7,"label") <- "Cum. Days of Treatment (7th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_8,"label") <- "Cum. Days of Treatment (8th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_9,"label") <- "Cum. Days of Treatment (9th Treatment)"
attr(prueba2$cum_dias_trat_sin_na_10,"label") <-"Cum. Days of Treatment (10th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_1,"label") <- "Days of Treatment (1st Treatment)"
attr(prueba2$dias_treat_imp_sin_na_2,"label") <- "Days of Treatment (2nd Treatment)"
attr(prueba2$dias_treat_imp_sin_na_3,"label") <- "Days of Treatment (3rd Treatment)"
attr(prueba2$dias_treat_imp_sin_na_4,"label") <- "Days of Treatment (4th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_5,"label") <- "Days of Treatment (5th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_6,"label") <- "Days of Treatment (6th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_7,"label") <- "Days of Treatment (7th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_8,"label") <- "Days of Treatment (8th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_9,"label") <- "Days of Treatment (9th Treatment)"
attr(prueba2$dias_treat_imp_sin_na_10,"label") <-"Days of Treatment (10th Treatment)"
attr(prueba2$cum_diff_bet_treat_1,"label") <- "Cum. Diff Between Treatments (1st Treatment)"
attr(prueba2$cum_diff_bet_treat_2,"label") <- "Cum. Diff Between Treatments (2nd Treatment)"
attr(prueba2$cum_diff_bet_treat_3,"label") <- "Cum. Diff Between Treatments (3rd Treatment)"
attr(prueba2$cum_diff_bet_treat_4,"label") <- "Cum. Diff Between Treatments (4th Treatment)"
attr(prueba2$cum_diff_bet_treat_5,"label") <- "Cum. Diff Between Treatments (5th Treatment)"
attr(prueba2$cum_diff_bet_treat_6,"label") <- "Cum. Diff Between Treatments (6th Treatment)"
attr(prueba2$cum_diff_bet_treat_7,"label") <- "Cum. Diff Between Treatments (7th Treatment)"
attr(prueba2$cum_diff_bet_treat_8,"label") <- "Cum. Diff Between Treatments (8th Treatment)"
attr(prueba2$cum_diff_bet_treat_9,"label") <- "Cum. Diff Between Treatments (9th Treatment)"
attr(prueba2$cum_diff_bet_treat_10,"label") <-"Cum. Diff Between Treatments (10th Treatment)"
attr(prueba2$numero_de_hijos_mod_joel,"label") <-"Number of Children (Max. Value), adding 1 if pregnant at admission"
attr(prueba2$sex_abuse,"label") <-"Sexual abuse"
attr(prueba2$dom_violence,"label") <-"Domestic violence"
attr(prueba2$freq_cons_sus_prin,"label") <-"Frequency of drug use in the primary substance"
#n_treats mean_cum_diff_bet_treat_four mean_cum_diff_bet_treat_four cum_diff_bet_treat_four cum_dias_trat_sin_na_four diff_bet_treat_four dias_treat_imp_sin_na_four cnt_mod_cie_10_dg_cons_sus_or max_cum_diff_bet_treat max_cum_dias_trat_sin_na cum_diff_bet_treat_10 cum_dias_trat_sin_na_10 tipo_de_plan_2_mod
library(compareGroups)
table3 <- compareGroups::compareGroups(no_group ~ sexo_2+ escolaridad_rec+ estado_conyugal_2+ compromiso_biopsicosocial+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sexo_2=3,
escolaridad_rec=3,
estado_conyugal_2=3,
compromiso_biopsicosocial=2,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered= 3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 2,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table3)
#p.adjust(pvals, method = "BH")
restab3 <- createTable(table3,show.p.overall = F)
compareGroups::export2md(restab3, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Total"),
format="html",caption= "Summary descriptives table")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")
| Variables | Total |
|---|---|
| N=23979 | |
| Sexo Usuario/Sex of User: | |
| Men | 19850 (82.8%) |
| Women | 4129 (17.2%) |
| escolaridad_rec: | |
| 3-Completed primary school or less | 5634 (23.5%) |
| 2-Completed high school or less | 14518 (60.5%) |
| 1-More than high school | 3751 (15.6%) |
| ‘Missing’ | 76 (0.32%) |
| Estado Conyugal/Marital Status: | |
| Married/Shared living arrangements | 5127 (21.4%) |
| Separated/Divorced | 501 (2.09%) |
| Single | 18294 (76.3%) |
| Widower | 30 (0.13%) |
| ‘Missing’ | 27 (0.11%) |
| Biopsychosocial Compromise: | |
| 1-Mild | 2240 (9.34%) |
| 2-Moderate | 13949 (58.2%) |
| 3-Severe | 7352 (30.7%) |
| ‘Missing’ | 438 (1.83%) |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 15.0 [13.0;16.0] |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.4 [22.6;27.7] |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | |
| Alcohol | 10341 (43.1%) |
| Cocaine hydrochloride | 934 (3.90%) |
| Cocaine paste | 1054 (4.40%) |
| Marijuana | 9165 (38.2%) |
| Other | 411 (1.71%) |
| ‘Missing’ | 2074 (8.65%) |
| Starting Substance: | |
| Alcohol | 10182 (42.5%) |
| Cocaine hydrochloride | 970 (4.05%) |
| Marijuana | 9255 (38.6%) |
| Other | 398 (1.66%) |
| Cocaine paste | 1100 (4.59%) |
| ‘Missing’ | 2074 (8.65%) |
| Frequency of drug use in the primary substance: | |
| 1 day a week or more | 1616 (6.74%) |
| 2 to 3 days a week | 7059 (29.4%) |
| 4 to 6 days a week | 4105 (17.1%) |
| Daily | 9911 (41.3%) |
| Less than 1 day a week | 1166 (4.86%) |
| ‘Missing’ | 122 (0.51%) |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | |
| Smoked or Pulmonary Aspiration | 13902 (58.0%) |
| Intranasal (powder aspiration) | 5575 (23.2%) |
| Injected Intravenously or Intramuscularly | 16 (0.07%) |
| Oral (drunk or eaten) | 4470 (18.6%) |
| Other | 12 (0.05%) |
| ‘Missing’ | 4 (0.02%) |
| Whom you live with(cohabitation status) (Recoded) (f): | |
| Alone | 1335 (5.57%) |
| Family of origin | 15455 (64.5%) |
| With couple/children | 7189 (30.0%) |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] |
| Occupational Status Corrected(f): | |
| Employed | 10423 (43.5%) |
| Inactive | 2019 (8.42%) |
| Looking for a job for the first time | 97 (0.40%) |
| No activity | 1333 (5.56%) |
| Not seeking for work | 216 (0.90%) |
| Unemployed | 9891 (41.2%) |
| Occupational Category Corrected(f): | |
| Employer | 322 (1.34%) |
| Other | 222 (0.93%) |
| Salaried | 7039 (29.4%) |
| Self-employed | 1934 (8.07%) |
| Unpaid family labour | 65 (0.27%) |
| Volunteer worker | 39 (0.16%) |
| ‘Missing’ | 14358 (59.9%) |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | |
| Mayor o igual a 90 días | 17383 (72.5%) |
| Menos de 90 días | 6596 (27.5%) |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| With psychiatric comorbidity | 9522 (39.7%) |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 147 [84.0;254] |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] |
| Type of Plan (Independently of the Program): | |
| PAB | 9277 (38.7%) |
| PAI | 11390 (47.5%) |
| PR | 3286 (13.7%) |
| ‘Missing’ | 26 (0.11%) |
| Tenure status of households: | |
| Illegal Settlement | 191 (0.80%) |
| Others | 562 (2.34%) |
| Owner/Transferred dwellings/Pays Dividends | 7502 (31.3%) |
| Renting | 3872 (16.1%) |
| Stays temporarily with a relative | 10646 (44.4%) |
| ‘Missing’ | 1206 (5.03%) |
| Cum. Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Cum. Days of Treatment (2nd Treatment) | 318 [207;489] |
| Cum. Days of Treatment (3rd Treatment) | 485 [335;704] |
| Cum. Days of Treatment (4th Treatment) | 638 [433;897] |
| Cum. Days of Treatment (5th Treatment) | 805 [550;1041] |
| Cum. Days of Treatment (6th Treatment) | 944 [716;1152] |
| Cum. Days of Treatment (7th Treatment) | 1076 [888;1279] |
| Cum. Days of Treatment (8th Treatment) | 1192 [1152;1232] |
| Cum. Days of Treatment (9th Treatment) | 1403 [1403;1403] |
| Cum. Days of Treatment (10th Treatment) | 1622 [1622;1622] |
| Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Days of Treatment (2nd Treatment) | 137 [77.0;239] |
| Days of Treatment (3rd Treatment) | 134 [73.0;237] |
| Days of Treatment (4th Treatment) | 126 [70.0;224] |
| Days of Treatment (5th Treatment) | 143 [67.0;260] |
| Days of Treatment (6th Treatment) | 145 [76.8;199] |
| Days of Treatment (7th Treatment) | 120 [23.0;175] |
| Days of Treatment (8th Treatment) | 40.0 [29.5;84.5] |
| Days of Treatment (9th Treatment) | 211 [211;211] |
| Days of Treatment (10th Treatment) | 219 [219;219] |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] |
| Cum. Diff Between Treatments (2nd Treatment) | 801 [405;1390] |
| Cum. Diff Between Treatments (3rd Treatment) | 1155 [686;1680] |
| Cum. Diff Between Treatments (4th Treatment) | 1341 [886;1972] |
| Cum. Diff Between Treatments (5th Treatment) | 1458 [1047;1970] |
| Cum. Diff Between Treatments (6th Treatment) | 1509 [1160;2294] |
| Cum. Diff Between Treatments (7th Treatment) | 1188 [1184;1422] |
| Cum. Diff Between Treatments (8th Treatment) | 1706 [1706;1706] |
| Cum. Diff Between Treatments (9th Treatment) | 1944 [1944;1944] |
| Cum. Diff Between Treatments (10th Treatment) | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | |
| 1 | 18472 (77.0%) |
| 2 | 3908 (16.3%) |
| 3 | 1087 (4.53%) |
| 4 | 347 (1.45%) |
| 5 | 111 (0.46%) |
| 6 | 37 (0.15%) |
| 7 | 14 (0.06%) |
| 8 | 2 (0.01%) |
| 10 | 1 (0.00%) |
| Max. Cumulative Days of Treatment | 182 [98.0;332] |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] |
| Total count of Psychiatric & Drug dependence Diagnostics | 1.00 [1.00;2.00] |
| cnt_mod_cie_10_or: | |
| 0 | 9259 (38.6%) |
| 1 | 14314 (59.7%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | |
| 0 | 14457 (60.3%) |
| 1 | 9116 (38.0%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Days of Treatment (Fourth or those that follow) | 138 [78.8;227] |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 277 [140;485] |
| Cumulative Days of Treatment (Fourth or those that follow) | 692 [460;936] |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1412 [1031;1995] |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 163 [110;221] |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 340 [236;488] |
| Comorbidity ICD-10 (with amount of different diagnosis): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| One | 9116 (38.0%) |
| Two or more | 406 (1.69%) |
| No. of treatments with 18+ at admission between 2010 and 2019: | |
| 01 | 18475 (77.0%) |
| 02 | 3907 (16.3%) |
| 03 | 1085 (4.52%) |
| 04 or more | 512 (2.14%) |
| Sexual abuse: | |
| No sexual abuse | 18880 (78.7%) |
| Sexual abuse | 289 (1.21%) |
| ‘Missing’ | 4810 (20.1%) |
| Domestic violence: | |
| No domestic violence | 14304 (59.7%) |
| Domestic violence | 4865 (20.3%) |
| ‘Missing’ | 4810 (20.1%) |
| Note. Variables of C1 dataset had to be standardized before comparison; | |
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | |
| Categorical variables are presented as number (%) |
Primary substance
Show code
table4 <- compareGroups::compareGroups(sus_principal_mod ~ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence= 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table4)
#p.adjust(pvals, method = "BH")
restab4 <- createTable(table4, show.p.overall = T)
compareGroups::export2md(restab4, size=9, first.strip=T, hide.no="no", position="center",
format="html",caption= "Summary descriptives by Primary Substance at Admission")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")
| Alcohol | Cocaine hydrochloride | Marijuana | Other | Cocaine paste | p.overall | |
|---|---|---|---|---|---|---|
| N=4142 | N=5394 | N=2872 | N=295 | N=11276 | ||
| Sexo Usuario/Sex of User: | <0.001 | |||||
| Men | 3321 (80.2%) | 4474 (82.9%) | 2356 (82.0%) | 223 (75.6%) | 9476 (84.0%) | |
| Women | 821 (19.8%) | 920 (17.1%) | 516 (18.0%) | 72 (24.4%) | 1800 (16.0%) | |
| escolaridad_rec: | . | |||||
| 3-Completed primary school or less | 900 (21.7%) | 873 (16.2%) | 536 (18.7%) | 62 (21.0%) | 3263 (28.9%) | |
| 2-Completed high school or less | 2376 (57.4%) | 3441 (63.8%) | 1714 (59.7%) | 152 (51.5%) | 6835 (60.6%) | |
| 1-More than high school | 852 (20.6%) | 1063 (19.7%) | 614 (21.4%) | 79 (26.8%) | 1143 (10.1%) | |
| ‘Missing’ | 14 (0.34%) | 17 (0.32%) | 8 (0.28%) | 2 (0.68%) | 35 (0.31%) | |
| Biopsychosocial Compromise: | <0.001 | |||||
| 1-Mild | 647 (15.6%) | 538 (9.97%) | 442 (15.4%) | 19 (6.44%) | 594 (5.27%) | |
| 2-Moderate | 2601 (62.8%) | 3339 (61.9%) | 1795 (62.5%) | 156 (52.9%) | 6058 (53.7%) | |
| 3-Severe | 814 (19.7%) | 1406 (26.1%) | 560 (19.5%) | 115 (39.0%) | 4457 (39.5%) | |
| ‘Missing’ | 80 (1.93%) | 111 (2.06%) | 75 (2.61%) | 5 (1.69%) | 167 (1.48%) | |
| Estado Conyugal/Marital Status: | . | |||||
| Married/Shared living arrangements | 1019 (24.6%) | 1350 (25.0%) | 465 (16.2%) | 36 (12.2%) | 2257 (20.0%) | |
| Separated/Divorced | 108 (2.61%) | 128 (2.37%) | 48 (1.67%) | 8 (2.71%) | 209 (1.85%) | |
| Single | 3005 (72.5%) | 3908 (72.5%) | 2352 (81.9%) | 250 (84.7%) | 8779 (77.9%) | |
| Widower | 5 (0.12%) | 3 (0.06%) | 4 (0.14%) | 1 (0.34%) | 17 (0.15%) | |
| ‘Missing’ | 5 (0.12%) | 5 (0.09%) | 3 (0.10%) | 0 (0.00%) | 14 (0.12%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 15.0 [13.0;16.0] | 15.0 [13.0;16.0] | 15.0 [13.0;16.0] | 14.0 [13.0;16.0] | 14.0 [13.0;16.0] | <0.001 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.7 [23.1;27.9] | 25.6 [23.0;27.8] | 23.6 [21.2;26.5] | 24.3 [21.1;27.8] | 25.5 [22.8;27.8] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | 0.000 | |||||
| Alcohol | 3256 (78.6%) | 2366 (43.9%) | 982 (34.2%) | 79 (26.8%) | 3658 (32.4%) | |
| Cocaine hydrochloride | 44 (1.06%) | 604 (11.2%) | 22 (0.77%) | 4 (1.36%) | 260 (2.31%) | |
| Cocaine paste | 28 (0.68%) | 57 (1.06%) | 32 (1.11%) | 2 (0.68%) | 935 (8.29%) | |
| Marijuana | 513 (12.4%) | 1913 (35.5%) | 1490 (51.9%) | 121 (41.0%) | 5128 (45.5%) | |
| Other | 43 (1.04%) | 65 (1.21%) | 41 (1.43%) | 65 (22.0%) | 197 (1.75%) | |
| ‘Missing’ | 258 (6.23%) | 389 (7.21%) | 305 (10.6%) | 24 (8.14%) | 1098 (9.74%) | |
| Starting Substance: | . | |||||
| Alcohol | 3249 (78.4%) | 2335 (43.3%) | 973 (33.9%) | 79 (26.8%) | 3546 (31.4%) | |
| Cocaine hydrochloride | 45 (1.09%) | 627 (11.6%) | 24 (0.84%) | 4 (1.36%) | 270 (2.39%) | |
| Marijuana | 520 (12.6%) | 1922 (35.6%) | 1498 (52.2%) | 121 (41.0%) | 5194 (46.1%) | |
| Other | 41 (0.99%) | 63 (1.17%) | 39 (1.36%) | 65 (22.0%) | 190 (1.68%) | |
| Cocaine paste | 29 (0.70%) | 58 (1.08%) | 33 (1.15%) | 2 (0.68%) | 978 (8.67%) | |
| ‘Missing’ | 258 (6.23%) | 389 (7.21%) | 305 (10.6%) | 24 (8.14%) | 1098 (9.74%) | |
| Frequency of drug use in the primary substance: | . | |||||
| 1 day a week or more | 384 (9.27%) | 502 (9.31%) | 125 (4.35%) | 14 (4.75%) | 591 (5.24%) | |
| 2 to 3 days a week | 1817 (43.9%) | 1934 (35.9%) | 639 (22.2%) | 52 (17.6%) | 2617 (23.2%) | |
| 4 to 6 days a week | 758 (18.3%) | 941 (17.4%) | 416 (14.5%) | 42 (14.2%) | 1948 (17.3%) | |
| Daily | 964 (23.3%) | 1630 (30.2%) | 1579 (55.0%) | 166 (56.3%) | 5572 (49.4%) | |
| Less than 1 day a week | 196 (4.73%) | 354 (6.56%) | 99 (3.45%) | 19 (6.44%) | 498 (4.42%) | |
| ‘Missing’ | 23 (0.56%) | 33 (0.61%) | 14 (0.49%) | 2 (0.68%) | 50 (0.44%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | |||||
| Smoked or Pulmonary Aspiration | 0 (0.00%) | 0 (0.00%) | 2872 (100%) | 34 (11.5%) | 10996 (97.5%) | |
| Intranasal (powder aspiration) | 0 (0.00%) | 5394 (100%) | 0 (0.00%) | 14 (4.75%) | 167 (1.48%) | |
| Injected Intravenously or Intramuscularly | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 16 (5.42%) | 0 (0.00%) | |
| Oral (drunk or eaten) | 4142 (100%) | 0 (0.00%) | 0 (0.00%) | 225 (76.3%) | 103 (0.91%) | |
| Other | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 5 (1.69%) | 7 (0.06%) | |
| ‘Missing’ | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1 (0.34%) | 3 (0.03%) | |
| Whom you live with(cohabitation status) (Recoded) (f): | <0.001 | |||||
| Alone | 311 (7.51%) | 199 (3.69%) | 148 (5.15%) | 16 (5.42%) | 661 (5.86%) | |
| Family of origin | 2434 (58.8%) | 3384 (62.7%) | 2031 (70.7%) | 222 (75.3%) | 7384 (65.5%) | |
| With couple/children | 1397 (33.7%) | 1811 (33.6%) | 693 (24.1%) | 57 (19.3%) | 3231 (28.7%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] | 1.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 1.00 [0.00;2.00] | <0.001 |
| Occupational Status Corrected(f): | . | |||||
| Employed | 2371 (57.2%) | 2833 (52.5%) | 1284 (44.7%) | 96 (32.5%) | 3839 (34.0%) | |
| Inactive | 410 (9.90%) | 368 (6.82%) | 418 (14.6%) | 43 (14.6%) | 780 (6.92%) | |
| Looking for a job for the first time | 16 (0.39%) | 14 (0.26%) | 22 (0.77%) | 2 (0.68%) | 43 (0.38%) | |
| No activity | 163 (3.94%) | 226 (4.19%) | 200 (6.96%) | 27 (9.15%) | 717 (6.36%) | |
| Not seeking for work | 20 (0.48%) | 48 (0.89%) | 24 (0.84%) | 5 (1.69%) | 119 (1.06%) | |
| Unemployed | 1162 (28.1%) | 1905 (35.3%) | 924 (32.2%) | 122 (41.4%) | 5778 (51.2%) | |
| Occupational Category Corrected(f): | . | |||||
| Employer | 88 (2.12%) | 83 (1.54%) | 34 (1.18%) | 2 (0.68%) | 115 (1.02%) | |
| Other | 48 (1.16%) | 46 (0.85%) | 30 (1.04%) | 4 (1.36%) | 94 (0.83%) | |
| Salaried | 1618 (39.1%) | 1929 (35.8%) | 878 (30.6%) | 50 (16.9%) | 2564 (22.7%) | |
| Self-employed | 399 (9.63%) | 559 (10.4%) | 217 (7.56%) | 23 (7.80%) | 736 (6.53%) | |
| Unpaid family labour | 5 (0.12%) | 16 (0.30%) | 11 (0.38%) | 0 (0.00%) | 33 (0.29%) | |
| Volunteer worker | 7 (0.17%) | 8 (0.15%) | 9 (0.31%) | 1 (0.34%) | 14 (0.12%) | |
| ‘Missing’ | 1977 (47.7%) | 2753 (51.0%) | 1693 (58.9%) | 215 (72.9%) | 7720 (68.5%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | <0.001 | |||||
| Mayor o igual a 90 días | 3214 (77.6%) | 3998 (74.1%) | 2184 (76.0%) | 221 (74.9%) | 7766 (68.9%) | |
| Menos de 90 días | 928 (22.4%) | 1396 (25.9%) | 688 (24.0%) | 74 (25.1%) | 3510 (31.1%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | <0.001 | |||||
| Without psychiatric comorbidity | 1652 (39.9%) | 2156 (40.0%) | 1100 (38.3%) | 75 (25.4%) | 4276 (37.9%) | |
| Diagnosis unknown (under study) | 720 (17.4%) | 1141 (21.2%) | 568 (19.8%) | 59 (20.0%) | 2710 (24.0%) | |
| With psychiatric comorbidity | 1770 (42.7%) | 2097 (38.9%) | 1204 (41.9%) | 161 (54.6%) | 4290 (38.0%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 165 [96.0;281] | 150 [87.0;251] | 157 [91.0;274] | 161 [89.0;298] | 135 [75.8;240] | <0.001 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.005 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | . | |||||
| PAB | 2120 (51.2%) | 2321 (43.0%) | 1380 (48.1%) | 88 (29.8%) | 3368 (29.9%) | |
| PAI | 1811 (43.7%) | 2605 (48.3%) | 1331 (46.3%) | 175 (59.3%) | 5468 (48.5%) | |
| PR | 208 (5.02%) | 465 (8.62%) | 157 (5.47%) | 31 (10.5%) | 2425 (21.5%) | |
| ‘Missing’ | 3 (0.07%) | 3 (0.06%) | 4 (0.14%) | 1 (0.34%) | 15 (0.13%) | |
| Tenure status of households: | . | |||||
| Illegal Settlement | 18 (0.43%) | 22 (0.41%) | 22 (0.77%) | 3 (1.02%) | 126 (1.12%) | |
| Others | 88 (2.12%) | 109 (2.02%) | 90 (3.13%) | 4 (1.36%) | 271 (2.40%) | |
| Owner/Transferred dwellings/Pays Dividends | 1403 (33.9%) | 1454 (27.0%) | 945 (32.9%) | 90 (30.5%) | 3610 (32.0%) | |
| Renting | 821 (19.8%) | 941 (17.4%) | 445 (15.5%) | 53 (18.0%) | 1612 (14.3%) | |
| Stays temporarily with a relative | 1648 (39.8%) | 2647 (49.1%) | 1216 (42.3%) | 135 (45.8%) | 5000 (44.3%) | |
| ‘Missing’ | 164 (3.96%) | 221 (4.10%) | 154 (5.36%) | 10 (3.39%) | 657 (5.83%) | |
| Cum. Days of Treatment (1st Treatment) | 165 [96.0;281] | 150 [87.2;251] | 157 [91.0;274] | 161 [89.0;300] | 135 [76.0;240] | <0.001 |
| Cum. Days of Treatment (2nd Treatment) | 367 [235;577] | 320 [212;491] | 351 [239;520] | 402 [288;592] | 305 [196;468] | <0.001 |
| Cum. Days of Treatment (3rd Treatment) | 550 [383;784] | 483 [344;704] | 500 [384;676] | 600 [411;820] | 473 [320;690] | 0.013 |
| Cum. Days of Treatment (4th Treatment) | 810 [573;994] | 659 [378;840] | 595 [437;780] | 913 [766;1162] | 608 [432;874] | 0.013 |
| Cum. Days of Treatment (5th Treatment) | 597 [430;972] | 854 [506;1000] | 866 [776;925] | 2000 [2000;2000] | 786 [554;1067] | 0.364 |
| Cum. Days of Treatment (6th Treatment) | 1022 [669;1087] | 803 [727;1080] | 984 [940;995] | 2230 [2230;2230] | 949 [714;1175] | 0.471 |
| Cum. Days of Treatment (7th Treatment) | 876 [778;975] | 887 [581;957] | 1348 [1348;1348] | . [.;.] | 1178 [1029;1300] | 0.101 |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1192 [1152;1232] | . |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1403 [1403;1403] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1622 [1622;1622] | . |
| Days of Treatment (1st Treatment) | 165 [96.0;281] | 150 [87.0;251] | 157 [91.0;274] | 161 [89.0;298] | 135 [75.8;240] | <0.001 |
| Days of Treatment (2nd Treatment) | 160 [92.0;278] | 139 [79.0;241] | 147 [80.2;262] | 215 [106;307] | 129 [72.0;224] | <0.001 |
| Days of Treatment (3rd Treatment) | 135 [81.0;232] | 140 [75.0;223] | 138 [73.2;251] | 108 [33.0;189] | 133 [72.0;239] | 0.881 |
| Days of Treatment (4th Treatment) | 152 [70.2;336] | 124 [61.5;217] | 128 [80.5;206] | 75.0 [46.5;195] | 124 [70.2;215] | 0.505 |
| Days of Treatment (5th Treatment) | 182 [76.8;232] | 151 [74.5;226] | 330 [213;388] | 588 [588;588] | 141 [63.0;254] | 0.268 |
| Days of Treatment (6th Treatment) | 67.0 [64.0;175] | 85.0 [36.5;180] | 144 [130;195] | 230 [230;230] | 154 [106;198] | 0.373 |
| Days of Treatment (7th Treatment) | 31.0 [20.5;41.5] | 175 [99.0;215] | 364 [364;364] | . [.;.] | 120 [28.0;146] | 0.223 |
| Days of Treatment (8th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 40.0 [29.5;84.5] | . |
| Days of Treatment (9th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 211 [211;211] | . |
| Days of Treatment (10th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 219 [219;219] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;74.0] | <0.001 |
| Cum. Diff Between Treatments (2nd Treatment) | 812 [413;1310] | 799 [405;1420] | 950 [533;1661] | 947 [733;1511] | 774 [392;1366] | 0.120 |
| Cum. Diff Between Treatments (3rd Treatment) | 1075 [645;1639] | 1221 [794;1732] | 1209 [696;1821] | 1274 [858;1290] | 1146 [680;1667] | 0.867 |
| Cum. Diff Between Treatments (4th Treatment) | 1424 [1098;2117] | 1141 [888;2078] | 1428 [1187;1640] | 1398 [1398;1398] | 1384 [880;1978] | 0.971 |
| Cum. Diff Between Treatments (5th Treatment) | 1695 [960;1700] | 1497 [1072;2131] | 1446 [1432;1668] | 1415 [1415;1415] | 1470 [1062;1992] | 0.992 |
| Cum. Diff Between Treatments (6th Treatment) | 1628 [1295;1961] | 2411 [1953;2594] | 1827 [1827;1827] | . [.;.] | 1287 [1088;1906] | 0.452 |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1188 [1184;1422] | . |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1706 [1706;1706] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | . [.;.] | . [.;.] | . [.;.] | 1944 [1944;1944] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | |||||
| 1 | 3435 (82.9%) | 4367 (81.0%) | 2422 (84.3%) | 246 (83.4%) | 8002 (71.0%) | |
| 2 | 537 (13.0%) | 781 (14.5%) | 328 (11.4%) | 35 (11.9%) | 2227 (19.7%) | |
| 3 | 116 (2.80%) | 176 (3.26%) | 91 (3.17%) | 11 (3.73%) | 693 (6.15%) | |
| 4 | 46 (1.11%) | 43 (0.80%) | 25 (0.87%) | 2 (0.68%) | 231 (2.05%) | |
| 5 | 3 (0.07%) | 17 (0.32%) | 3 (0.10%) | 0 (0.00%) | 88 (0.78%) | |
| 6 | 3 (0.07%) | 7 (0.13%) | 2 (0.07%) | 1 (0.34%) | 24 (0.21%) | |
| 7 | 2 (0.05%) | 3 (0.06%) | 1 (0.03%) | 0 (0.00%) | 8 (0.07%) | |
| 8 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 2 (0.02%) | |
| 10 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1 (0.01%) | |
| Max. Cumulative Days of Treatment | 194 [106;350] | 181 [99.0;313] | 182 [98.0;331] | 200 [95.0;337] | 179 [93.0;336] | <0.001 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;123] | <0.001 |
| Total count of Psychiatric & Drug dependence Diagnostics: | . | |||||
| 0 | 1108 (26.8%) | 954 (17.7%) | 719 (25.0%) | 33 (11.2%) | 1450 (12.9%) | |
| 1 | 1965 (47.4%) | 2914 (54.0%) | 1377 (47.9%) | 138 (46.8%) | 6303 (55.9%) | |
| 2 | 1003 (24.2%) | 1438 (26.7%) | 745 (25.9%) | 115 (39.0%) | 3376 (29.9%) | |
| 3 | 60 (1.45%) | 75 (1.39%) | 28 (0.97%) | 9 (3.05%) | 137 (1.21%) | |
| 4 | 6 (0.14%) | 13 (0.24%) | 3 (0.10%) | 0 (0.00%) | 10 (0.09%) | |
| cnt_mod_cie_10_or: | . | |||||
| 0 | 1652 (39.9%) | 2156 (40.0%) | 1100 (38.3%) | 75 (25.4%) | 4276 (37.9%) | |
| 1 | 2405 (58.1%) | 3139 (58.2%) | 1729 (60.2%) | 207 (70.2%) | 6834 (60.6%) | |
| 2 | 74 (1.79%) | 84 (1.56%) | 38 (1.32%) | 13 (4.41%) | 156 (1.38%) | |
| 3 | 11 (0.27%) | 15 (0.28%) | 5 (0.17%) | 0 (0.00%) | 10 (0.09%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | . | |||||
| 0 | 2372 (57.3%) | 3297 (61.1%) | 1668 (58.1%) | 134 (45.4%) | 6986 (62.0%) | |
| 1 | 1685 (40.7%) | 1998 (37.0%) | 1161 (40.4%) | 148 (50.2%) | 4124 (36.6%) | |
| 2 | 74 (1.79%) | 84 (1.56%) | 38 (1.32%) | 13 (4.41%) | 156 (1.38%) | |
| 3 | 11 (0.27%) | 15 (0.28%) | 5 (0.17%) | 0 (0.00%) | 10 (0.09%) | |
| Days of Treatment (Fourth or those that follow) | 149 [74.8;322] | 138 [74.0;218] | 144 [99.0;218] | 75.0 [46.5;226] | 136 [79.1;223] | 0.719 |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 308 [170;552] | 340 [171;558] | 330 [138;365] | 70.5 [70.5;70.5] | 256 [132;483] | 0.695 |
| Cumulative Days of Treatment (Fourth or those that follow) | 831 [585;1012] | 676 [412;902] | 683 [499;809] | 913 [766;1397] | 684 [449;929] | 0.047 |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1694 [1127;2134] | 1370 [1032;2078] | 1520 [1385;1640] | 1406 [1406;1406] | 1411 [1018;2019] | 0.940 |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 202 [144;248] | 161 [98.3;201] | 149 [125;192] | 228 [191;302] | 159 [108;217] | 0.014 |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 356 [272;518] | 295 [241;493] | 347 [296;407] | 316 [316;316] | 341 [228;483] | 0.990 |
| Comorbidity ICD-10 (with amount of different diagnosis): | . | |||||
| Without psychiatric comorbidity | 1652 (39.9%) | 2156 (40.0%) | 1100 (38.3%) | 75 (25.4%) | 4276 (37.9%) | |
| Diagnosis unknown (under study) | 720 (17.4%) | 1141 (21.2%) | 568 (19.8%) | 59 (20.0%) | 2710 (24.0%) | |
| One | 1685 (40.7%) | 1998 (37.0%) | 1161 (40.4%) | 148 (50.2%) | 4124 (36.6%) | |
| Two or more | 85 (2.05%) | 99 (1.84%) | 43 (1.50%) | 13 (4.41%) | 166 (1.47%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | <0.001 | |||||
| 01 | 3435 (82.9%) | 4368 (81.0%) | 2422 (84.3%) | 246 (83.4%) | 8004 (71.0%) | |
| 02 | 537 (13.0%) | 781 (14.5%) | 328 (11.4%) | 36 (12.2%) | 2225 (19.7%) | |
| 03 | 116 (2.80%) | 175 (3.24%) | 91 (3.17%) | 10 (3.39%) | 693 (6.15%) | |
| 04 or more | 54 (1.30%) | 70 (1.30%) | 31 (1.08%) | 3 (1.02%) | 354 (3.14%) | |
| Sexual abuse: | . | |||||
| No sexual abuse | 3147 (76.0%) | 4124 (76.5%) | 2263 (78.8%) | 227 (76.9%) | 9119 (80.9%) | |
| Sexual abuse | 60 (1.45%) | 65 (1.21%) | 23 (0.80%) | 6 (2.03%) | 135 (1.20%) | |
| ‘Missing’ | 935 (22.6%) | 1205 (22.3%) | 586 (20.4%) | 62 (21.0%) | 2022 (17.9%) | |
| Domestic violence: | <0.001 | |||||
| No domestic violence | 2278 (55.0%) | 3228 (59.8%) | 1764 (61.4%) | 161 (54.6%) | 6873 (61.0%) | |
| Domestic violence | 929 (22.4%) | 961 (17.8%) | 522 (18.2%) | 72 (24.4%) | 2381 (21.1%) | |
| ‘Missing’ | 935 (22.6%) | 1205 (22.3%) | 586 (20.4%) | 62 (21.0%) | 2022 (17.9%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | ||||||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||||
| Categorical variables are presented as number (%) |
Living with
Show code
table5 <- compareGroups::compareGroups(con_quien_vive_joel ~ sus_principal_mod+ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sus_principal_mod=3,
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T,
max.xlev = 10,
max.ylev = 10
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table5)
#p.adjust(pvals, method = "BH")
restab5 <- createTable(table5, show.p.overall = T)
compareGroups::export2md(restab5, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Alone","Family of origin", "With couple", "P-value"),
format="html",caption= "Summary descriptives by With whom they live")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")
| Variables | Alone | Family of origin | With couple | P-value |
|---|---|---|---|---|
| N=1335 | N=15455 | N=7189 | ||
| Sustancia Principal de Consumo (Sólo más frecuentes)(f)/Primary or Main Substance of Consumption at Admission (Only more frequent)(f): | <0.001 | |||
| Alcohol | 311 (23.3%) | 2434 (15.7%) | 1397 (19.4%) | |
| Cocaine hydrochloride | 199 (14.9%) | 3384 (21.9%) | 1811 (25.2%) | |
| Marijuana | 148 (11.1%) | 2031 (13.1%) | 693 (9.64%) | |
| Other | 16 (1.20%) | 222 (1.44%) | 57 (0.79%) | |
| Cocaine paste | 661 (49.5%) | 7384 (47.8%) | 3231 (44.9%) | |
| Sexo Usuario/Sex of User: | <0.001 | |||
| Men | 1171 (87.7%) | 13154 (85.1%) | 5525 (76.9%) | |
| Women | 164 (12.3%) | 2301 (14.9%) | 1664 (23.1%) | |
| escolaridad_rec: | . | |||
| 3-Completed primary school or less | 359 (26.9%) | 3333 (21.6%) | 1942 (27.0%) | |
| 2-Completed high school or less | 767 (57.5%) | 9420 (61.0%) | 4331 (60.2%) | |
| 1-More than high school | 201 (15.1%) | 2660 (17.2%) | 890 (12.4%) | |
| ‘Missing’ | 8 (0.60%) | 42 (0.27%) | 26 (0.36%) | |
| Biopsychosocial Compromise: | <0.001 | |||
| 1-Mild | 92 (6.89%) | 1328 (8.59%) | 820 (11.4%) | |
| 2-Moderate | 642 (48.1%) | 8856 (57.3%) | 4451 (61.9%) | |
| 3-Severe | 588 (44.0%) | 4988 (32.3%) | 1776 (24.7%) | |
| ‘Missing’ | 13 (0.97%) | 283 (1.83%) | 142 (1.98%) | |
| Estado Conyugal/Marital Status: | . | |||
| Married/Shared living arrangements | 101 (7.57%) | 858 (5.55%) | 4168 (58.0%) | |
| Separated/Divorced | 55 (4.12%) | 361 (2.34%) | 85 (1.18%) | |
| Single | 1171 (87.7%) | 14203 (91.9%) | 2920 (40.6%) | |
| Widower | 1 (0.07%) | 24 (0.16%) | 5 (0.07%) | |
| ‘Missing’ | 7 (0.52%) | 9 (0.06%) | 11 (0.15%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 14.0 [13.0;16.0] | 15.0 [13.0;16.0] | 15.0 [13.0;16.0] | <0.001 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 26.3 [23.5;28.3] | 24.7 [22.0;27.3] | 26.4 [24.1;28.3] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | <0.001 | |||
| Alcohol | 587 (44.0%) | 6505 (42.1%) | 3249 (45.2%) | |
| Cocaine hydrochloride | 37 (2.77%) | 548 (3.55%) | 349 (4.85%) | |
| Cocaine paste | 68 (5.09%) | 636 (4.12%) | 350 (4.87%) | |
| Marijuana | 511 (38.3%) | 6157 (39.8%) | 2497 (34.7%) | |
| Other | 26 (1.95%) | 250 (1.62%) | 135 (1.88%) | |
| ‘Missing’ | 106 (7.94%) | 1359 (8.79%) | 609 (8.47%) | |
| Starting Substance: | <0.001 | |||
| Alcohol | 581 (43.5%) | 6385 (41.3%) | 3216 (44.7%) | |
| Cocaine hydrochloride | 39 (2.92%) | 571 (3.69%) | 360 (5.01%) | |
| Marijuana | 514 (38.5%) | 6228 (40.3%) | 2513 (35.0%) | |
| Other | 24 (1.80%) | 245 (1.59%) | 129 (1.79%) | |
| Cocaine paste | 71 (5.32%) | 667 (4.32%) | 362 (5.04%) | |
| ‘Missing’ | 106 (7.94%) | 1359 (8.79%) | 609 (8.47%) | |
| Frequency of drug use in the primary substance: | <0.001 | |||
| 1 day a week or more | 55 (4.12%) | 904 (5.85%) | 657 (9.14%) | |
| 2 to 3 days a week | 328 (24.6%) | 4334 (28.0%) | 2397 (33.3%) | |
| 4 to 6 days a week | 204 (15.3%) | 2737 (17.7%) | 1164 (16.2%) | |
| Daily | 699 (52.4%) | 6768 (43.8%) | 2444 (34.0%) | |
| Less than 1 day a week | 45 (3.37%) | 633 (4.10%) | 488 (6.79%) | |
| ‘Missing’ | 4 (0.30%) | 79 (0.51%) | 39 (0.54%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | |||
| Smoked or Pulmonary Aspiration | 798 (59.8%) | 9266 (60.0%) | 3838 (53.4%) | |
| Intranasal (powder aspiration) | 205 (15.4%) | 3505 (22.7%) | 1865 (25.9%) | |
| Injected Intravenously or Intramuscularly | 2 (0.15%) | 12 (0.08%) | 2 (0.03%) | |
| Oral (drunk or eaten) | 328 (24.6%) | 2662 (17.2%) | 1480 (20.6%) | |
| Other | 1 (0.07%) | 7 (0.05%) | 4 (0.06%) | |
| ‘Missing’ | 1 (0.07%) | 3 (0.02%) | 0 (0.00%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] | 0.00 [0.00;1.00] | 1.00 [1.00;2.00] | 0.000 |
| Occupational Status Corrected(f): | <0.001 | |||
| Employed | 648 (48.5%) | 5601 (36.2%) | 4174 (58.1%) | |
| Inactive | 57 (4.27%) | 1231 (7.97%) | 731 (10.2%) | |
| Looking for a job for the first time | 3 (0.22%) | 78 (0.50%) | 16 (0.22%) | |
| No activity | 86 (6.44%) | 1015 (6.57%) | 232 (3.23%) | |
| Not seeking for work | 34 (2.55%) | 154 (1.00%) | 28 (0.39%) | |
| Unemployed | 507 (38.0%) | 7376 (47.7%) | 2008 (27.9%) | |
| Occupational Category Corrected(f): | . | |||
| Employer | 14 (1.05%) | 163 (1.05%) | 145 (2.02%) | |
| Other | 17 (1.27%) | 128 (0.83%) | 77 (1.07%) | |
| Salaried | 432 (32.4%) | 3833 (24.8%) | 2774 (38.6%) | |
| Self-employed | 119 (8.91%) | 953 (6.17%) | 862 (12.0%) | |
| Unpaid family labour | 1 (0.07%) | 52 (0.34%) | 12 (0.17%) | |
| Volunteer worker | 2 (0.15%) | 25 (0.16%) | 12 (0.17%) | |
| ‘Missing’ | 750 (56.2%) | 10301 (66.7%) | 3307 (46.0%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | <0.001 | |||
| Mayor o igual a 90 días | 865 (64.8%) | 11229 (72.7%) | 5289 (73.6%) | |
| Menos de 90 días | 470 (35.2%) | 4226 (27.3%) | 1900 (26.4%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| With psychiatric comorbidity | 536 (40.1%) | 6415 (41.5%) | 2571 (35.8%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.015 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | . | |||
| PAB | 391 (29.3%) | 5506 (35.6%) | 3380 (47.0%) | |
| PAI | 586 (43.9%) | 7508 (48.6%) | 3296 (45.8%) | |
| PR | 356 (26.7%) | 2423 (15.7%) | 507 (7.05%) | |
| ‘Missing’ | 2 (0.15%) | 18 (0.12%) | 6 (0.08%) | |
| Tenure status of households: | 0.000 | |||
| Illegal Settlement | 53 (3.97%) | 48 (0.31%) | 90 (1.25%) | |
| Others | 74 (5.54%) | 374 (2.42%) | 114 (1.59%) | |
| Owner/Transferred dwellings/Pays Dividends | 258 (19.3%) | 5345 (34.6%) | 1899 (26.4%) | |
| Renting | 555 (41.6%) | 1154 (7.47%) | 2163 (30.1%) | |
| Stays temporarily with a relative | 145 (10.9%) | 7879 (51.0%) | 2622 (36.5%) | |
| ‘Missing’ | 250 (18.7%) | 655 (4.24%) | 301 (4.19%) | |
| Cum. Days of Treatment (1st Treatment) | 125 [68.0;224] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Cum. Days of Treatment (2nd Treatment) | 285 [174;423] | 320 [206;493] | 326 [218;489] | <0.001 |
| Cum. Days of Treatment (3rd Treatment) | 463 [260;644] | 474 [326;700] | 522 [361;730] | 0.008 |
| Cum. Days of Treatment (4th Treatment) | 526 [339;755] | 633 [426;878] | 671 [486;941] | 0.059 |
| Cum. Days of Treatment (5th Treatment) | 690 [532;1120] | 798 [541;1047] | 876 [576;1021] | 0.604 |
| Cum. Days of Treatment (6th Treatment) | 460 [356;565] | 897 [712;1153] | 1006 [868;1144] | 0.106 |
| Cum. Days of Treatment (7th Treatment) | 477 [376;578] | 1165 [993;1331] | 1076 [1074;1254] | 0.100 |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | 1152 [1131;1172] | 1273 [1273;1273] | 0.221 |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | 1403 [1403;1403] | . [.;.] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | 1622 [1622;1622] | . [.;.] | . |
| Days of Treatment (1st Treatment) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Days of Treatment (2nd Treatment) | 119 [57.0;204] | 137 [76.0;239] | 139 [83.0;245] | <0.001 |
| Days of Treatment (3rd Treatment) | 127 [71.0;220] | 127 [70.0;229] | 148 [84.8;246] | 0.020 |
| Days of Treatment (4th Treatment) | 143 [57.2;248] | 125 [70.0;215] | 131 [71.0;231] | 0.997 |
| Days of Treatment (5th Treatment) | 199 [139;335] | 138 [67.8;254] | 145 [59.2;260] | 0.567 |
| Days of Treatment (6th Treatment) | 28.0 [17.5;38.5] | 149 [121;195] | 116 [68.5;205] | 0.121 |
| Days of Treatment (7th Treatment) | 16.5 [13.2;19.8] | 169 [58.5;244] | 53.0 [52.0;120] | 0.075 |
| Days of Treatment (8th Treatment) | . [.;.] | 84.5 [62.2;107] | 19.0 [19.0;19.0] | 0.221 |
| Days of Treatment (9th Treatment) | . [.;.] | 211 [211;211] | . [.;.] | . |
| Days of Treatment (10th Treatment) | . [.;.] | 219 [219;219] | . [.;.] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.812 |
| Cum. Diff Between Treatments (2nd Treatment) | 715 [380;1315] | 779 [396;1355] | 872 [441;1492] | 0.075 |
| Cum. Diff Between Treatments (3rd Treatment) | 920 [405;1812] | 1128 [674;1648] | 1235 [770;1697] | 0.436 |
| Cum. Diff Between Treatments (4th Treatment) | 1290 [1044;2121] | 1196 [843;1722] | 1448 [1149;2190] | 0.034 |
| Cum. Diff Between Treatments (5th Treatment) | 2020 [1858;2182] | 1470 [1091;1768] | 1418 [944;2384] | 0.513 |
| Cum. Diff Between Treatments (6th Treatment) | 2536 [2415;2657] | 1576 [1244;2085] | 1221 [1015;1287] | 0.174 |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | 1418 [1300;1537] | 1188 [1188;1188] | 1.000 |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | 1706 [1706;1706] | . [.;.] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | 1944 [1944;1944] | . [.;.] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | |||
| 1 | 1033 (77.4%) | 11903 (77.0%) | 5536 (77.0%) | |
| 2 | 207 (15.5%) | 2540 (16.4%) | 1161 (16.1%) | |
| 3 | 71 (5.32%) | 673 (4.35%) | 343 (4.77%) | |
| 4 | 15 (1.12%) | 233 (1.51%) | 99 (1.38%) | |
| 5 | 7 (0.52%) | 69 (0.45%) | 35 (0.49%) | |
| 6 | 0 (0.00%) | 27 (0.17%) | 10 (0.14%) | |
| 7 | 2 (0.15%) | 8 (0.05%) | 4 (0.06%) | |
| 8 | 0 (0.00%) | 1 (0.01%) | 1 (0.01%) | |
| 10 | 0 (0.00%) | 1 (0.01%) | 0 (0.00%) | |
| Max. Cumulative Days of Treatment | 154 [80.0;304] | 183 [98.0;332] | 186 [100;338] | <0.001 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.883 |
| Total count of Psychiatric & Drug dependence Diagnostics: | . | |||
| 0 | 224 (16.8%) | 2493 (16.1%) | 1547 (21.5%) | |
| 1 | 695 (52.1%) | 8175 (52.9%) | 3827 (53.2%) | |
| 2 | 399 (29.9%) | 4536 (29.3%) | 1742 (24.2%) | |
| 3 | 16 (1.20%) | 226 (1.46%) | 67 (0.93%) | |
| 4 | 1 (0.07%) | 25 (0.16%) | 6 (0.08%) | |
| cnt_mod_cie_10_or: | . | |||
| 0 | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| 1 | 860 (64.4%) | 9441 (61.1%) | 4013 (55.8%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | . | |||
| 0 | 799 (59.9%) | 9040 (58.5%) | 4618 (64.2%) | |
| 1 | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Days of Treatment (Fourth or those that follow) | 149 [51.8;234] | 138 [77.8;225] | 137 [85.4;236] | 0.966 |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 464 [238;588] | 258 [112;416] | 288 [164;682] | 0.064 |
| Cumulative Days of Treatment (Fourth or those that follow) | 590 [364;936] | 683 [454;932] | 727 [518;999] | 0.083 |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1760 [1044;2121] | 1345 [943;1759] | 1518 [1174;2252] | 0.081 |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 133 [91.0;209] | 162 [107;218] | 171 [116;229] | 0.080 |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 348 [261;497] | 312 [222;424] | 373 [286;544] | 0.064 |
| Comorbidity ICD-10 (with amount of different diagnosis): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| One | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| Two or more | 22 (1.65%) | 296 (1.92%) | 88 (1.22%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | 0.426 | |||
| 01 | 1034 (77.5%) | 11905 (77.0%) | 5536 (77.0%) | |
| 02 | 206 (15.4%) | 2540 (16.4%) | 1161 (16.1%) | |
| 03 | 71 (5.32%) | 671 (4.34%) | 343 (4.77%) | |
| 04 or more | 24 (1.80%) | 339 (2.19%) | 149 (2.07%) | |
| Sexual abuse: | 0.034 | |||
| No sexual abuse | 1053 (78.9%) | 12207 (79.0%) | 5620 (78.2%) | |
| Sexual abuse | 27 (2.02%) | 175 (1.13%) | 87 (1.21%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Domestic violence: | <0.001 | |||
| No domestic violence | 745 (55.8%) | 9509 (61.5%) | 4050 (56.3%) | |
| Domestic violence | 335 (25.1%) | 2873 (18.6%) | 1657 (23.0%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | ||||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||
| Categorical variables are presented as number (%) |
Readmissions
Show code
table6 <- compareGroups::compareGroups(had_readm ~ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = prueba2,
include.miss = T,
var.equal=T
)#cie_10 cat_ocupacional estatus_ocupacional
pvals <- getResults(table6)
#p.adjust(pvals, method = "BH")
restab6 <- createTable(table6, show.p.overall = T)
compareGroups::export2md(restab6, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Had no Readmissions","Had Readmissions", "P-value"),
format="html",caption= "Table 5. Summary descriptives by Readmissions")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")
| Variables | Had no Readmissions | Had Readmissions | P-value |
|---|---|---|---|
| N=18475 | N=5504 | ||
| Sexo Usuario/Sex of User: | 0.001 | ||
| Men | 15373 (83.2%) | 4477 (81.3%) | |
| Women | 3102 (16.8%) | 1027 (18.7%) | |
| escolaridad_rec: | <0.001 | ||
| 3-Completed primary school or less | 4166 (22.5%) | 1468 (26.7%) | |
| 2-Completed high school or less | 11046 (59.8%) | 3472 (63.1%) | |
| 1-More than high school | 3202 (17.3%) | 549 (9.97%) | |
| ‘Missing’ | 61 (0.33%) | 15 (0.27%) | |
| Biopsychosocial Compromise: | <0.001 | ||
| 1-Mild | 1840 (9.96%) | 400 (7.27%) | |
| 2-Moderate | 11058 (59.9%) | 2891 (52.5%) | |
| 3-Severe | 5244 (28.4%) | 2108 (38.3%) | |
| ‘Missing’ | 333 (1.80%) | 105 (1.91%) | |
| Estado Conyugal/Marital Status: | 0.105 | ||
| Married/Shared living arrangements | 3918 (21.2%) | 1209 (22.0%) | |
| Separated/Divorced | 397 (2.15%) | 104 (1.89%) | |
| Single | 14121 (76.4%) | 4173 (75.8%) | |
| Widower | 18 (0.10%) | 12 (0.22%) | |
| ‘Missing’ | 21 (0.11%) | 6 (0.11%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 15.0 [13.0;16.0] | 14.0 [13.0;16.0] | 0.088 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.3 [22.5;27.7] | 25.5 [22.8;27.8] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | <0.001 | ||
| Alcohol | 7890 (42.7%) | 2451 (44.5%) | |
| Cocaine hydrochloride | 688 (3.72%) | 246 (4.47%) | |
| Cocaine paste | 667 (3.61%) | 387 (7.03%) | |
| Marijuana | 6926 (37.5%) | 2239 (40.7%) | |
| Other | 298 (1.61%) | 113 (2.05%) | |
| ‘Missing’ | 2006 (10.9%) | 68 (1.24%) | |
| Starting Substance: | <0.001 | ||
| Alcohol | 7839 (42.4%) | 2343 (42.6%) | |
| Cocaine hydrochloride | 703 (3.81%) | 267 (4.85%) | |
| Marijuana | 6951 (37.6%) | 2304 (41.9%) | |
| Other | 295 (1.60%) | 103 (1.87%) | |
| Cocaine paste | 681 (3.69%) | 419 (7.61%) | |
| ‘Missing’ | 2006 (10.9%) | 68 (1.24%) | |
| Frequency of drug use in the primary substance: | 0.003 | ||
| 1 day a week or more | 1272 (6.88%) | 344 (6.25%) | |
| 2 to 3 days a week | 5493 (29.7%) | 1566 (28.5%) | |
| 4 to 6 days a week | 3140 (17.0%) | 965 (17.5%) | |
| Daily | 7539 (40.8%) | 2372 (43.1%) | |
| Less than 1 day a week | 935 (5.06%) | 231 (4.20%) | |
| ‘Missing’ | 96 (0.52%) | 26 (0.47%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | ||
| Smoked or Pulmonary Aspiration | 10250 (55.5%) | 3652 (66.4%) | |
| Intranasal (powder aspiration) | 4504 (24.4%) | 1071 (19.5%) | |
| Injected Intravenously or Intramuscularly | 15 (0.08%) | 1 (0.02%) | |
| Oral (drunk or eaten) | 3695 (20.0%) | 775 (14.1%) | |
| Other | 7 (0.04%) | 5 (0.09%) | |
| ‘Missing’ | 4 (0.02%) | 0 (0.00%) | |
| Whom you live with(cohabitation status) (Recoded) (f): | 0.935 | ||
| Alone | 1034 (5.60%) | 301 (5.47%) | |
| Family of origin | 11905 (64.4%) | 3550 (64.5%) | |
| With couple/children | 5536 (30.0%) | 1653 (30.0%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission | 1.00 [0.00;1.00] | 1.00 [0.00;2.00] | <0.001 |
| Occupational Status Corrected(f): | <0.001 | ||
| Employed | 8278 (44.8%) | 2145 (39.0%) | |
| Inactive | 1562 (8.45%) | 457 (8.30%) | |
| Looking for a job for the first time | 72 (0.39%) | 25 (0.45%) | |
| No activity | 1021 (5.53%) | 312 (5.67%) | |
| Not seeking for work | 157 (0.85%) | 59 (1.07%) | |
| Unemployed | 7385 (40.0%) | 2506 (45.5%) | |
| Occupational Category Corrected(f): | <0.001 | ||
| Employer | 256 (1.39%) | 66 (1.20%) | |
| Other | 176 (0.95%) | 46 (0.84%) | |
| Salaried | 5527 (29.9%) | 1512 (27.5%) | |
| Self-employed | 1584 (8.57%) | 350 (6.36%) | |
| Unpaid family labour | 50 (0.27%) | 15 (0.27%) | |
| Volunteer worker | 30 (0.16%) | 9 (0.16%) | |
| ‘Missing’ | 10852 (58.7%) | 3506 (63.7%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | 0.547 | ||
| Mayor o igual a 90 días | 13375 (72.4%) | 4008 (72.8%) | |
| Menos de 90 días | 5100 (27.6%) | 1496 (27.2%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | 0.134 | ||
| Without psychiatric comorbidity | 7191 (38.9%) | 2068 (37.6%) | |
| Diagnosis unknown (under study) | 4006 (21.7%) | 1192 (21.7%) | |
| With psychiatric comorbidity | 7278 (39.4%) | 2244 (40.8%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 146 [84.0;254] | 148 [85.0;254] | 0.399 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.949 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | <0.001 | ||
| PAB | 7376 (39.9%) | 1901 (34.5%) | |
| PAI | 8901 (48.2%) | 2489 (45.2%) | |
| PR | 2184 (11.8%) | 1102 (20.0%) | |
| ‘Missing’ | 14 (0.08%) | 12 (0.22%) | |
| Tenure status of households: | 0.056 | ||
| Illegal Settlement | 145 (0.78%) | 46 (0.84%) | |
| Others | 424 (2.29%) | 138 (2.51%) | |
| Owner/Transferred dwellings/Pays Dividends | 5782 (31.3%) | 1720 (31.2%) | |
| Renting | 2999 (16.2%) | 873 (15.9%) | |
| Stays temporarily with a relative | 8239 (44.6%) | 2407 (43.7%) | |
| ‘Missing’ | 886 (4.80%) | 320 (5.81%) | |
| Cum. Days of Treatment (1st Treatment) | 146 [84.0;254] | 148 [85.0;255] | 0.380 |
| Cum. Days of Treatment (2nd Treatment) | . [.;.] | 318 [207;489] | . |
| Cum. Days of Treatment (3rd Treatment) | . [.;.] | 485 [335;704] | . |
| Cum. Days of Treatment (4th Treatment) | . [.;.] | 638 [433;897] | . |
| Cum. Days of Treatment (5th Treatment) | . [.;.] | 805 [550;1041] | . |
| Cum. Days of Treatment (6th Treatment) | . [.;.] | 944 [716;1152] | . |
| Cum. Days of Treatment (7th Treatment) | . [.;.] | 1076 [888;1279] | . |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | 1192 [1152;1232] | . |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | 1403 [1403;1403] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | 1622 [1622;1622] | . |
| Days of Treatment (1st Treatment) | 146 [84.0;254] | 148 [85.0;254] | 0.399 |
| Days of Treatment (2nd Treatment) | . [.;.] | 137 [77.0;239] | . |
| Days of Treatment (3rd Treatment) | . [.;.] | 134 [73.0;237] | . |
| Days of Treatment (4th Treatment) | . [.;.] | 126 [70.0;224] | . |
| Days of Treatment (5th Treatment) | . [.;.] | 143 [67.0;260] | . |
| Days of Treatment (6th Treatment) | . [.;.] | 145 [76.8;199] | . |
| Days of Treatment (7th Treatment) | . [.;.] | 120 [23.0;175] | . |
| Days of Treatment (8th Treatment) | . [.;.] | 40.0 [29.5;84.5] | . |
| Days of Treatment (9th Treatment) | . [.;.] | 211 [211;211] | . |
| Days of Treatment (10th Treatment) | . [.;.] | 219 [219;219] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 407 [149;926] | 0.000 |
| Cum. Diff Between Treatments (2nd Treatment) | . [.;.] | 801 [405;1390] | . |
| Cum. Diff Between Treatments (3rd Treatment) | . [.;.] | 1155 [686;1680] | . |
| Cum. Diff Between Treatments (4th Treatment) | . [.;.] | 1341 [886;1972] | . |
| Cum. Diff Between Treatments (5th Treatment) | . [.;.] | 1458 [1047;1970] | . |
| Cum. Diff Between Treatments (6th Treatment) | . [.;.] | 1509 [1160;2294] | . |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | 1188 [1184;1422] | . |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | 1706 [1706;1706] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | 1944 [1944;1944] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | ||
| 1 | 18472 (100.0%) | 0 (0.00%) | |
| 2 | 3 (0.02%) | 3905 (70.9%) | |
| 3 | 0 (0.00%) | 1087 (19.7%) | |
| 4 | 0 (0.00%) | 347 (6.30%) | |
| 5 | 0 (0.00%) | 111 (2.02%) | |
| 6 | 0 (0.00%) | 37 (0.67%) | |
| 7 | 0 (0.00%) | 14 (0.25%) | |
| 8 | 0 (0.00%) | 2 (0.04%) | |
| 10 | 0 (0.00%) | 1 (0.02%) | |
| Max. Cumulative Days of Treatment | 146 [84.0;254] | 380 [239;583] | 0.000 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 630 [232;1268] | 0.000 |
| Total count of Psychiatric & Drug dependence Diagnostics: | <0.001 | ||
| 0 | 3463 (18.7%) | 801 (14.6%) | |
| 1 | 9719 (52.6%) | 2978 (54.1%) | |
| 2 | 5036 (27.3%) | 1641 (29.8%) | |
| 3 | 232 (1.26%) | 77 (1.40%) | |
| 4 | 25 (0.14%) | 7 (0.13%) | |
| cnt_mod_cie_10_or: | 0.238 | ||
| 0 | 7191 (38.9%) | 2068 (37.6%) | |
| 1 | 10972 (59.4%) | 3342 (60.7%) | |
| 2 | 278 (1.50%) | 87 (1.58%) | |
| 3 | 34 (0.18%) | 7 (0.13%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | 0.226 | ||
| 0 | 11197 (60.6%) | 3260 (59.2%) | |
| 1 | 6966 (37.7%) | 2150 (39.1%) | |
| 2 | 278 (1.50%) | 87 (1.58%) | |
| 3 | 34 (0.18%) | 7 (0.13%) | |
| Days of Treatment (Fourth or those that follow) | . [.;.] | 138 [78.8;227] | . |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | . [.;.] | 277 [140;485] | . |
| Cumulative Days of Treatment (Fourth or those that follow) | . [.;.] | 692 [460;936] | . |
| Cumulative Difference Between Treatments (Fifth or those that follow) | . [.;.] | 1412 [1031;1995] | . |
| Average Cumulative Days of Treatment (Fourth or those that follow) | . [.;.] | 163 [110;221] | . |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | . [.;.] | 340 [236;488] | . |
| Comorbidity ICD-10 (with amount of different diagnosis): | 0.254 | ||
| Without psychiatric comorbidity | 7191 (38.9%) | 2068 (37.6%) | |
| Diagnosis unknown (under study) | 4006 (21.7%) | 1192 (21.7%) | |
| One | 6966 (37.7%) | 2150 (39.1%) | |
| Two or more | 312 (1.69%) | 94 (1.71%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | 0.000 | ||
| 01 | 18475 (100%) | 0 (0.00%) | |
| 02 | 0 (0.00%) | 3907 (71.0%) | |
| 03 | 0 (0.00%) | 1085 (19.7%) | |
| 04 or more | 0 (0.00%) | 512 (9.30%) | |
| Sexual abuse: | <0.001 | ||
| No sexual abuse | 14413 (78.0%) | 4467 (81.2%) | |
| Sexual abuse | 203 (1.10%) | 86 (1.56%) | |
| ‘Missing’ | 3859 (20.9%) | 951 (17.3%) | |
| Domestic violence: | <0.001 | ||
| No domestic violence | 10974 (59.4%) | 3330 (60.5%) | |
| Domestic violence | 3642 (19.7%) | 1223 (22.2%) | |
| ‘Missing’ | 3859 (20.9%) | 951 (17.3%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | |||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | |||
| Categorical variables are presented as number (%) |
Show code
library(ggplot2)
jpg_path<-rstudioapi::getSourceEditorContext()$path
if (grepl("CISS Fondecyt",jpg_path)==T){
jpg_path<-paste0("C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
} else if (grepl("andre",jpg_path)==T){
jpg_path<-paste0('C:/Users/andre/Desktop/SUD_CL/')
} else if (grepl("E:",jpg_path)==T){
jpg_path<-paste0("E:/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
} else {
jpg_path<-paste0("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/")
}
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(dias_treat_imp_sin_na_1,dias_treat_imp_sin_na_2,dias_treat_imp_sin_na_3,dias_treat_imp_sin_na_four) %>% #mean_cum_dias_trat_sin_na_1
tidyr::gather(option,value) %>%
dplyr::mutate(option=dplyr::case_when(option=="dias_treat_imp_sin_na_1"~"01",
option=="dias_treat_imp_sin_na_2"~"02",
option=="dias_treat_imp_sin_na_3"~"03",
option=="dias_treat_imp_sin_na_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Days in Treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
Figure 1: Figura 1. Días de Tratamiento, según la posición del tratamiento en cada usuario usuario
Show code
library(ggplot2)
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(diff_bet_treat_1,diff_bet_treat_2,diff_bet_treat_3,diff_bet_treat_four) %>% #mean_cum_dias_trat_sin_na_1
tidyr::gather(option,value) %>%
dplyr::mutate(option=dplyr::case_when(option=="diff_bet_treat_1"~"01",
option=="diff_bet_treat_2"~"02",
option=="diff_bet_treat_3"~"03",
option=="diff_bet_treat_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
#geom_label(inherit.aes = F,aes(x = option, y=value, label = max(count, na.rm=T)), vjust = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option) %>% slice(1),
aes(label = paste0(count, " Obs."), x = option), y = -0.5)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Difference (in days) with the posterior treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
Figure 2: Figura 2. Diferencia en Días con el Tratamiento Siguiente, según la posición del tratamiento en cada usuario usuario
Show code
library(ggplot2)
f3<-
prueba2 %>%
# dplyr::group_by(hash_key)%>%
# dplyr::mutate(n_hash=n())%>%
#dplyr::filter(n_hash>1)%>%
# slice(1) %>%
# ungroup() %>%
dplyr::select(con_quien_vive_joel,dias_treat_imp_sin_na_1,dias_treat_imp_sin_na_2,dias_treat_imp_sin_na_3,dias_treat_imp_sin_na_four) %>% #mean_cum_dias_trat_sin_na_1con_quien_vive_joel
tidyr::gather(option,value,-con_quien_vive_joel) %>%
dplyr::mutate(option=dplyr::case_when(option=="dias_treat_imp_sin_na_1"~"01",
option=="dias_treat_imp_sin_na_2"~"02",
option=="dias_treat_imp_sin_na_3"~"03",
option=="dias_treat_imp_sin_na_four"~"04 or more")) %>%
dplyr::mutate(option=factor(option)) %>%
dplyr::group_by(con_quien_vive_joel,option) %>%
dplyr::mutate(count = length(na.omit(value))) %>%
dplyr::ungroup() %>%
ggplot(aes(x = option, y=value,group= option)) +
stat_summary(fun = mean, geom="bar",alpha=.8, na.rm = T)+
stat_summary(fun = median, geom="point", na.rm = T)+
stat_summary(fun = median,
fun.min = function(x) quantile(x,.25, na.rm = T),
fun.max = function(x) quantile(x,.75, na.rm = T),
geom = "errorbar", width = 0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option,con_quien_vive_joel) %>% slice(1),
aes(label = paste0("n=",count), x = option), y = -0.5)+
geom_label(inherit.aes = FALSE, data = . %>% group_by(option,con_quien_vive_joel) %>% slice(1),
aes(label = paste0("n=",count), x = option), y = -0.5)+
facet_wrap(~con_quien_vive_joel)+
theme_bw()+
theme(plot.caption = element_text(hjust = 0, face= "italic",size=9))+
#geom_bar(stat = "identity")+
#geom_errorbar() +
labs(x="Number of Treatment of Each User", y="Days in Treatment",caption=paste0("Note. Bars=Means, Dots= Medians, Error bars= Percentiles 25 and 75"))
f3
Figure 3: Figura 3. Días de Tratamiento, según la posición del tratamiento en cada usuario usuario
Imputation (October 2021)
We generated a plot to see all the missing values in the sample.
Show code
#<div style="border: 1px solid #ddd; padding: 5px; overflow-y: scroll; height:400px; overflow-x: scroll; width:100%">
library(dplyr)
library(ggplot2)
#
# sexo_2=3,
# escolaridad_rec=3, 76 (0.32%)
# estado_conyugal_2=3, 27 (0.11%)
# edad_ini_sus_prin=2, 2755 (11,5%)
# edad_al_ing=2,
# sus_ini_mod=3, 2074 (8.65%)
# sus_ini_mod_mvv=3, 2074 (8.65%)
# freq_cons_sus_prin=3, 122 (0.51%)
# via_adm_sus_prin_act=3, 4 (0.02%)
# con_quien_vive_joel=3,
# numero_de_hijos_mod_joel=2, 293 (1,2%)
# condicion_ocupacional_corr=3, 14358 (59.9%)
# cat_ocupacional_corr=3, 14358 (59.9%)
# abandono_temprano=3,
# dg_cie_10_rec=3,
# dias_treat_imp_sin_na=2,
# cnt_mod_cie_10_or=3,
# cnt_diagnostico_trs_fisico=2,
# cnt_otros_probl_at_sm_or=2,
# tipo_de_plan_2_mod=3, 26 (0.11%)
# cnt_mod_cie_10_dg_cons_sus_or= 3,
# dg_total_cie_10 = 3,
# comorbidity_icd_10 = 3,
# compromiso_biopsicosocial 438 (1.83%)
# tenencia_de_la_vivienda_mod 1206 (5.03%)
vector_variables<-
c("sexo_2", "escolaridad_rec", "estado_conyugal_2", "edad_ini_cons", "sus_ini_mod_mvv", "freq_cons_sus_prin", "condicion_ocupacional_corr", "via_adm_sus_prin_act", "con_quien_vive_joel", "numero_de_hijos_mod_joel", "condicion_ocupacional_corr", "dias_treat_imp_sin_na", "cnt_mod_cie_10_or", "tipo_de_plan_2_mod", "comorbidity_icd_10", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod","tipo_centro","macrozona","compromiso_biopsicosocial")
missing.values<-
prueba2 %>%
rowwise %>%
dplyr::mutate_at(.vars = vars(vector_variables),
.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::summarise_at(vars(vector_variables),~sum(.))
#t(missing.values)
miss_val_bar<-
data.table::melt(missing.values) %>%
mutate(perc=scales::percent(value/nrow(prueba2))) %>%
arrange(desc(perc))
plot_miss<-
missing.values %>%
data.table::melt() %>% #condicion_ocupacional_corr
dplyr::filter(!variable %in% c("row", "hash_key", "dias_treat_imp_sin_na", "dup")) %>%
dplyr::mutate(perc= value/sum(nrow(prueba2))) %>%
dplyr::mutate(label_text= paste0("Variable= ",variable,"<br>n= ",value,"<br>",scales::percent(round(perc,3)))) %>%
dplyr::mutate(perc=perc*100) %>%
ggplot() +
geom_bar(aes(x=factor(variable), y=perc,label= label_text), stat = 'identity') +
sjPlot::theme_sjplot()+
# scale_y_continuous(limits=c(0,1), labels=percent)+
theme(axis.text.x = element_text(angle = 90, hjust = 1, size=9))+
labs(x=NULL, y="% of Missing Values", caption=paste0("Nota. Percentage of missing values (n= ",format(nrow(prueba2),big.mark=","),")"))
ggplotly(plot_miss, tooltip = c("label_text"))%>% layout(xaxis= list(showticklabels = T), height = 600, width=800) %>% layout(yaxis = list(tickformat='%', range = c(0, 15)))
Figure 4: Figure 3. Bar plot of Percentage of Missing Values per Variables at Basline
Show code
#</div>
From the figure above, we could see that the Age of Onset of Drug Use
(edad_ini_cons), Starting substance
(sus_ini_mod_mvv), the Substance Tenure status of
households (tenencia_de_la_vivienda_mod) and the
Biopsychosocial Involvement (compromiso_biopsicosocial) had
a proportion of missing values, but no greater than 5%. This is why we
imputed these values under MAR assumption.
Show code
vector_variables_only_for_imputation<-
c("row","hash_key", "edad_al_ing", "sexo_2", "escolaridad_rec", "estado_conyugal_2", "edad_ini_cons", "sus_ini_mod_mvv", "freq_cons_sus_prin", "condicion_ocupacional_corr", "via_adm_sus_prin_act", "con_quien_vive_joel", "numero_de_hijos_mod_joel", "condicion_ocupacional_corr", "dias_treat_imp_sin_na", "tipo_de_plan_2_mod", "comorbidity_icd_10", "compromiso_biopsicosocial", "tenencia_de_la_vivienda_mod", "tipo_centro", "macrozona", "compromiso_biopsicosocial", "sus_ini_mod")#cnt_mod_cie_10_or",
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
prueba2_miss<-
prueba2 %>%
#dplyr::group_by(hash_key) %>%
#dplyr::mutate(rn=row_number()) %>%
#dplyr::ungroup() %>%
#:#:#:#:#:#:#:#:#:#:#:
# ORDINALIZAR LAS VARIABLES ORDINALES:
dplyr::select_(.dots = vector_variables_only_for_imputation) %>%
dplyr::mutate(numero_de_hijos_mod_joel=dplyr::case_when(numero_de_hijos_mod_joel>3~"4 or more",
T~as.character(numero_de_hijos_mod_joel))) %>%
#dplyr::select(-hash_key) %>%
as.data.frame()
#CONS_C1_df_dup_SEP_2020 %>% janitor::tabyl(evaluacindelprocesoteraputico)
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:#:
time_before_missforest<-Sys.time()
# for (var in colnames(prueba2_miss)) {
# attr(prueba2_miss[[deparse(as.name(var))]], "label") <- NULL
# }
library(missRanger)
set.seed(2125)
prueba2_imp <- missRanger(
prueba2_miss,
formula = . ~ . - row - hash_key,
num.trees = 200,
returnOOB=T,
maxiter=50,
verbose = 2,
seed = 2125)
time_after_missforest<-Sys.time()
paste0("Time in imputation process: ");time_after_missforest-time_before_missforest
Show code
ggplotly(DataExplorer::plot_missing(prueba2,missing_only = T))
Figure 5: Figure 4. Plot of missing values in the original sample
Figure 5: Figure 4. Plot of missing values in the original sample
Descriptives
Show code
attr(prueba2_imp$numero_de_hijos_mod_joel,"label") <-"Number of Children (Max. Value), adding 1 if pregnant at admission"
attr(prueba2$cnt_mod_cie_10_or,"label") <- "Total count of Psychiatric Diagnostics"
attr(prueba2_imp$escolaridad_rec,"label") <- "Educational Attainment"
table6_imp <- compareGroups::compareGroups(no_group ~ sexo_2+ escolaridad_rec+ estado_conyugal_2+ compromiso_biopsicosocial+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ con_quien_vive_joel+ numero_de_hijos_mod_joel+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+ cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+ max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+ dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sexo_2=3,
escolaridad_rec=3,
estado_conyugal_2=3,
compromiso_biopsicosocial=2,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
con_quien_vive_joel=3,
numero_de_hijos_mod_joel=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered= 3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 2,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence))%>% dplyr::mutate(no_group=1),
include.miss = T,
var.equal=T
)
pvals <- getResults(table6_imp)
restab6_imp <- createTable(table6_imp,show.p.overall = F)
compareGroups::export2md(restab6_imp, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Total"),
format="html",caption= "Summary descriptives")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")
| Variables | Total |
|---|---|
| N=23979 | |
| Sexo Usuario/Sex of User: | |
| Men | 19850 (82.8%) |
| Women | 4129 (17.2%) |
| Educational Attainment: | |
| 3-Completed primary school or less | 5638 (23.5%) |
| 2-Completed high school or less | 14586 (60.8%) |
| 1-More than high school | 3755 (15.7%) |
| Estado Conyugal/Marital Status: | |
| Married/Shared living arrangements | 5137 (21.4%) |
| Separated/Divorced | 501 (2.09%) |
| Single | 18311 (76.4%) |
| Widower | 30 (0.13%) |
| Biopsychosocial Compromise: | |
| 1-Mild | 2245 (9.36%) |
| 2-Moderate | 14309 (59.7%) |
| 3-Severe | 7425 (31.0%) |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 14.9 [13.0;16.0] |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 25.4 [22.6;27.7] |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | |
| Alcohol | 11212 (46.8%) |
| Cocaine hydrochloride | 936 (3.90%) |
| Cocaine paste | 1058 (4.41%) |
| Marijuana | 10362 (43.2%) |
| Other | 411 (1.71%) |
| Starting Substance: | |
| Alcohol | 11053 (46.1%) |
| Cocaine hydrochloride | 972 (4.05%) |
| Marijuana | 10452 (43.6%) |
| Other | 398 (1.66%) |
| Cocaine paste | 1104 (4.60%) |
| freq_cons_sus_prin: | |
| 1 day a week or more | 1616 (6.74%) |
| 2 to 3 days a week | 7128 (29.7%) |
| 4 to 6 days a week | 4108 (17.1%) |
| Daily | 9961 (41.5%) |
| Less than 1 day a week | 1166 (4.86%) |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | |
| Smoked or Pulmonary Aspiration | 13906 (58.0%) |
| Intranasal (powder aspiration) | 5575 (23.2%) |
| Injected Intravenously or Intramuscularly | 16 (0.07%) |
| Oral (drunk or eaten) | 4470 (18.6%) |
| Other | 12 (0.05%) |
| Whom you live with(cohabitation status) (Recoded) (f): | |
| Alone | 1335 (5.57%) |
| Family of origin | 15455 (64.5%) |
| With couple/children | 7189 (30.0%) |
| Number of Children (Max. Value), adding 1 if pregnant at admission: | |
| 0 | 9730 (40.6%) |
| 1 | 8664 (36.1%) |
| 2 | 3929 (16.4%) |
| 3 | 1218 (5.08%) |
| 4 or more | 438 (1.83%) |
| Occupational Status Corrected(f): | |
| Employed | 10423 (43.5%) |
| Inactive | 2019 (8.42%) |
| Looking for a job for the first time | 97 (0.40%) |
| No activity | 1333 (5.56%) |
| Not seeking for work | 216 (0.90%) |
| Unemployed | 9891 (41.2%) |
| Occupational Category Corrected(f): | |
| Employer | 322 (1.34%) |
| Other | 222 (0.93%) |
| Salaried | 7039 (29.4%) |
| Self-employed | 1934 (8.07%) |
| Unpaid family labour | 65 (0.27%) |
| Volunteer worker | 39 (0.16%) |
| ‘Missing’ | 14358 (59.9%) |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | |
| Mayor o igual a 90 días | 17383 (72.5%) |
| Menos de 90 días | 6596 (27.5%) |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| With psychiatric comorbidity | 9522 (39.7%) |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 147 [84.0;254] |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] |
| Type of Plan (Independently of the Program): | |
| PAB | 9282 (38.7%) |
| PAI | 11407 (47.6%) |
| PR | 3290 (13.7%) |
| Tenure status of households: | |
| Illegal Settlement | 235 (0.98%) |
| Others | 566 (2.36%) |
| Owner/Transferred dwellings/Pays Dividends | 7759 (32.4%) |
| Renting | 4055 (16.9%) |
| Stays temporarily with a relative | 11364 (47.4%) |
| Cum. Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Cum. Days of Treatment (2nd Treatment) | 318 [207;489] |
| Cum. Days of Treatment (3rd Treatment) | 485 [335;704] |
| Cum. Days of Treatment (4th Treatment) | 638 [433;897] |
| Cum. Days of Treatment (5th Treatment) | 805 [550;1041] |
| Cum. Days of Treatment (6th Treatment) | 944 [716;1152] |
| Cum. Days of Treatment (7th Treatment) | 1076 [888;1279] |
| Cum. Days of Treatment (8th Treatment) | 1192 [1152;1232] |
| Cum. Days of Treatment (9th Treatment) | 1403 [1403;1403] |
| Cum. Days of Treatment (10th Treatment) | 1622 [1622;1622] |
| Days of Treatment (1st Treatment) | 147 [84.0;254] |
| Days of Treatment (2nd Treatment) | 137 [77.0;239] |
| Days of Treatment (3rd Treatment) | 134 [73.0;237] |
| Days of Treatment (4th Treatment) | 126 [70.0;224] |
| Days of Treatment (5th Treatment) | 143 [67.0;260] |
| Days of Treatment (6th Treatment) | 145 [76.8;199] |
| Days of Treatment (7th Treatment) | 120 [23.0;175] |
| Days of Treatment (8th Treatment) | 40.0 [29.5;84.5] |
| Days of Treatment (9th Treatment) | 211 [211;211] |
| Days of Treatment (10th Treatment) | 219 [219;219] |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] |
| Cum. Diff Between Treatments (2nd Treatment) | 801 [405;1390] |
| Cum. Diff Between Treatments (3rd Treatment) | 1155 [686;1680] |
| Cum. Diff Between Treatments (4th Treatment) | 1341 [886;1972] |
| Cum. Diff Between Treatments (5th Treatment) | 1458 [1047;1970] |
| Cum. Diff Between Treatments (6th Treatment) | 1509 [1160;2294] |
| Cum. Diff Between Treatments (7th Treatment) | 1188 [1184;1422] |
| Cum. Diff Between Treatments (8th Treatment) | 1706 [1706;1706] |
| Cum. Diff Between Treatments (9th Treatment) | 1944 [1944;1944] |
| Cum. Diff Between Treatments (10th Treatment) | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | |
| 1 | 18472 (77.0%) |
| 2 | 3908 (16.3%) |
| 3 | 1087 (4.53%) |
| 4 | 347 (1.45%) |
| 5 | 111 (0.46%) |
| 6 | 37 (0.15%) |
| 7 | 14 (0.06%) |
| 8 | 2 (0.01%) |
| 10 | 1 (0.00%) |
| Max. Cumulative Days of Treatment | 182 [98.0;332] |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] |
| Total count of Psychiatric & Drug dependence Diagnostics | 1.00 [1.00;2.00] |
| Total count of Psychiatric Diagnostics: | |
| 0 | 9259 (38.6%) |
| 1 | 14314 (59.7%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | |
| 0 | 14457 (60.3%) |
| 1 | 9116 (38.0%) |
| 2 | 365 (1.52%) |
| 3 | 41 (0.17%) |
| Days of Treatment (Fourth or those that follow) | 138 [78.8;227] |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 277 [140;485] |
| Cumulative Days of Treatment (Fourth or those that follow) | 692 [460;936] |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1412 [1031;1995] |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 163 [110;221] |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 340 [236;488] |
| Comorbidity ICD-10 (with amount of different diagnosis): | |
| Without psychiatric comorbidity | 9259 (38.6%) |
| Diagnosis unknown (under study) | 5198 (21.7%) |
| One | 9116 (38.0%) |
| Two or more | 406 (1.69%) |
| No. of treatments with 18+ at admission between 2010 and 2019: | |
| 01 | 18475 (77.0%) |
| 02 | 3907 (16.3%) |
| 03 | 1085 (4.52%) |
| 04 or more | 512 (2.14%) |
| Sexual abuse: | |
| No sexual abuse | 18880 (78.7%) |
| Sexual abuse | 289 (1.21%) |
| ‘Missing’ | 4810 (20.1%) |
| Domestic violence: | |
| No domestic violence | 14304 (59.7%) |
| Domestic violence | 4865 (20.3%) |
| ‘Missing’ | 4810 (20.1%) |
| Note. Variables of C1 dataset had to be standardized before comparison; | |
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | |
| Categorical variables are presented as number (%) |
Living with
Show code
table5_imp <- compareGroups::compareGroups(con_quien_vive_joel ~ sus_principal_mod+ sexo_2+ escolaridad_rec+ compromiso_biopsicosocial+ estado_conyugal_2+ edad_ini_cons+ edad_al_ing+ sus_ini_mod+ sus_ini_mod_mvv+ freq_cons_sus_prin+ via_adm_sus_prin_act+ numero_de_hijos_mod_joel+ numero_de_hijos_mod_joel_bin+ condicion_ocupacional_corr+ cat_ocupacional_corr+ abandono_temprano+ dg_cie_10_rec+ dias_treat_imp_sin_na+ cnt_diagnostico_trs_fisico+ cnt_otros_probl_at_sm_or+ tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod+ cum_dias_trat_sin_na_1+ cum_dias_trat_sin_na_2+ cum_dias_trat_sin_na_3+ cum_dias_trat_sin_na_4+ cum_dias_trat_sin_na_5+ cum_dias_trat_sin_na_6+ cum_dias_trat_sin_na_7+ cum_dias_trat_sin_na_8+ cum_dias_trat_sin_na_9+ cum_dias_trat_sin_na_10+ dias_treat_imp_sin_na_1+ dias_treat_imp_sin_na_2+ dias_treat_imp_sin_na_3+ dias_treat_imp_sin_na_4+ dias_treat_imp_sin_na_5+ dias_treat_imp_sin_na_6+ dias_treat_imp_sin_na_7+ dias_treat_imp_sin_na_8+ dias_treat_imp_sin_na_9+ dias_treat_imp_sin_na_10+ cum_diff_bet_treat_1+cum_diff_bet_treat_2+ cum_diff_bet_treat_3+ cum_diff_bet_treat_4+ cum_diff_bet_treat_5+ cum_diff_bet_treat_6+ cum_diff_bet_treat_7+ cum_diff_bet_treat_8+ cum_diff_bet_treat_9+ cum_diff_bet_treat_10+ duplicates_filtered+max_cum_dias_trat_sin_na+ max_cum_diff_bet_treat+ cnt_mod_cie_10_dg_cons_sus_or+ cnt_mod_cie_10_or+ dg_total_cie_10+dias_treat_imp_sin_na_four+ diff_bet_treat_four+ cum_dias_trat_sin_na_four+ cum_diff_bet_treat_four+ mean_cum_dias_trat_sin_na_four+ mean_cum_diff_bet_treat_four+ comorbidity_icd_10+ n_treats+ sex_abuse+ dom_violence,
method= c(sus_principal_mod=3,
sexo_2=3,
escolaridad_rec=3,
compromiso_biopsicosocial=2,
estado_conyugal_2=3,
edad_ini_cons=2,
edad_al_ing=2,
sus_ini_mod=3,
sus_ini_mod_mvv=3,
freq_cons_sus_prin=3,
via_adm_sus_prin_act=3,
numero_de_hijos_mod_joel=2,
numero_de_hijos_mod_joel_bin=2,
condicion_ocupacional_corr=3,
cat_ocupacional_corr=3,
abandono_temprano=3,
dg_cie_10_rec=3,
dias_treat_imp_sin_na=2,
cnt_mod_cie_10_or=3,
cnt_diagnostico_trs_fisico=2,
cnt_otros_probl_at_sm_or=2,
tipo_de_plan_2_mod=3,
tenencia_de_la_vivienda_mod=2,
cum_dias_trat_sin_na_1= 2,
cum_dias_trat_sin_na_2= 2,
cum_dias_trat_sin_na_3= 2,
cum_dias_trat_sin_na_4= 2,
cum_dias_trat_sin_na_5= 2,
cum_dias_trat_sin_na_6= 2,
cum_dias_trat_sin_na_7= 2,
cum_dias_trat_sin_na_8= 2,
cum_dias_trat_sin_na_9= 2,
cum_dias_trat_sin_na_10=2,
dias_treat_imp_sin_na_1= 2,
dias_treat_imp_sin_na_2= 2,
dias_treat_imp_sin_na_3= 2,
dias_treat_imp_sin_na_4= 2,
dias_treat_imp_sin_na_5= 2,
dias_treat_imp_sin_na_6= 2,
dias_treat_imp_sin_na_7= 2,
dias_treat_imp_sin_na_8= 2,
dias_treat_imp_sin_na_9= 2,
dias_treat_imp_sin_na_10=2,
cum_diff_bet_treat_1= 2,
cum_diff_bet_treat_2= 2,
cum_diff_bet_treat_3= 2,
cum_diff_bet_treat_4= 2,
cum_diff_bet_treat_5= 2,
cum_diff_bet_treat_6= 2,
cum_diff_bet_treat_7= 2,
cum_diff_bet_treat_8= 2,
cum_diff_bet_treat_9= 2,
cum_diff_bet_treat_10= 2,
duplicates_filtered=3,
max_cum_dias_trat_sin_na= 2,
max_cum_diff_bet_treat= 2,
cnt_mod_cie_10_dg_cons_sus_or= 3,
dg_total_cie_10 = 3,
comorbidity_icd_10 = 3,
dias_treat_imp_sin_na_four = 2,
diff_bet_treat_four = 2,
cum_dias_trat_sin_na_four = 2,
cum_diff_bet_treat_four = 2,
mean_cum_dias_trat_sin_na_four = 2,
mean_cum_diff_bet_treat_four = 2,
n_treats = 3,
sex_abuse = 3,
dom_violence = 3
),
data = cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence)) %>%
dplyr::mutate(numero_de_hijos_mod_joel_bin=dplyr::case_when(numero_de_hijos_mod_joel=="0"~"0",
!is.na(numero_de_hijos_mod_joel)~"One or more (or pregnant)")),
include.miss = T,
var.equal=T,
max.xlev = 10,
max.ylev = 10
)#cie_10 cat_ocupacional estatus_ocupacional
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
if(no_mostrar==1){
catVars_after_imp<-
cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence)) %>%
dplyr::mutate(numero_de_hijos_mod_joel_bin=dplyr::case_when(numero_de_hijos_mod_joel=="0"~"0",
!is.na(numero_de_hijos_mod_joel)~"One or more (or pregnant)")) %>%
dplyr::select_if(~!is.numeric(.x)) %>% names()
#prueba
cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
#
dplyr::select(prueba2,sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence)) %>%
dplyr::mutate(numero_de_hijos_mod_joel_bin=dplyr::case_when(numero_de_hijos_mod_joel=="0"~"0",
!is.na(numero_de_hijos_mod_joel)~"One or more (or pregnant)")) %>%
dplyr::select(catVars_after_imp) %>%
tidyr::gather(variable,measure, -con_quien_vive_joel) %>%
group_by(variable) %>%
do(chisq.test(.$con_quien_vive_joel, .$measure) %>% broom::tidy()) %>%
dplyr::mutate(p.value=ifelse(p.value<.001,"<0.001",sprintf("%1.3f",p.value))) %>%
dplyr::mutate(statistic=sprintf("%2.2f",statistic)) %>%
dplyr::mutate(report=paste0("X²(",parameter,", N=",nrow(prueba2),")=",statistic,ifelse(p.value=="<0.001",p.value, paste0("=",p.value)))) %>% copiar_nombres()
}
if(no_mostrar==1){
invisible("Prueba post-revisión para sacar el wilcox y test de asociaciones")
broom::tidy(wilcox.test(dias_treat_imp_sin_na ~ tipo_de_programa_2, data=CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados)) %>% dplyr::mutate(report=paste0("W"))
#effect size
broom::tidy(wilcox.test(dias_treat_imp_sin_na ~ tipo_de_programa_2, data=CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados))$statistic/sqrt(nrow(CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados))
broom::tidy(t.test(dias_treat_imp_sin_na ~ tipo_de_programa_2, data=CONS_C1_df_dup_SEP_2020_women_miss_after_imp_conservados)) %>%
dplyr::mutate(report=paste0("t(",round(parameter,0),")=",sprintf("%1.2f",statistic),",",ifelse(p.value<0.001,"<0.001", paste0("=",sprinf("f1.3f",p.value)))))
# t(19) = 3.1, p = .006.
}
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_
pvals <- getResults(table5_imp)
#p.adjust(pvals, method = "BH")
restab5_imp <- createTable(table5_imp, show.p.overall = T)
compareGroups::export2md(restab5_imp, size=9, first.strip=T, hide.no="no", position="center",col.names=c("Variables","Alone","Family of origin", "With couple", "P-value"),
format="html",caption= "Summary descriptives by With whom they live (with some imputed variables")%>%
kableExtra::add_footnote(c("Note. Variables of C1 dataset had to be standardized before comparison;", "Continuous variables are presented as Medians and Percentiles 25 and 75 were shown;", "Categorical variables are presented as number (%)"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::scroll_box(width = "100%", height = "600px")
| Variables | Alone | Family of origin | With couple | P-value |
|---|---|---|---|---|
| N=1335 | N=15455 | N=7189 | ||
| Sustancia Principal de Consumo (Sólo más frecuentes)(f)/Primary or Main Substance of Consumption at Admission (Only more frequent)(f): | <0.001 | |||
| Alcohol | 311 (23.3%) | 2434 (15.7%) | 1397 (19.4%) | |
| Cocaine hydrochloride | 199 (14.9%) | 3384 (21.9%) | 1811 (25.2%) | |
| Marijuana | 148 (11.1%) | 2031 (13.1%) | 693 (9.64%) | |
| Other | 16 (1.20%) | 222 (1.44%) | 57 (0.79%) | |
| Cocaine paste | 661 (49.5%) | 7384 (47.8%) | 3231 (44.9%) | |
| Sexo Usuario/Sex of User: | <0.001 | |||
| Men | 1171 (87.7%) | 13154 (85.1%) | 5525 (76.9%) | |
| Women | 164 (12.3%) | 2301 (14.9%) | 1664 (23.1%) | |
| Educational Attainment: | <0.001 | |||
| 3-Completed primary school or less | 359 (26.9%) | 3336 (21.6%) | 1943 (27.0%) | |
| 2-Completed high school or less | 774 (58.0%) | 9456 (61.2%) | 4356 (60.6%) | |
| 1-More than high school | 202 (15.1%) | 2663 (17.2%) | 890 (12.4%) | |
| Biopsychosocial Compromise: | <0.001 | |||
| 1-Mild | 92 (6.89%) | 1332 (8.62%) | 821 (11.4%) | |
| 2-Moderate | 649 (48.6%) | 9080 (58.8%) | 4580 (63.7%) | |
| 3-Severe | 594 (44.5%) | 5043 (32.6%) | 1788 (24.9%) | |
| Estado Conyugal/Marital Status: | . | |||
| Married/Shared living arrangements | 101 (7.57%) | 858 (5.55%) | 4178 (58.1%) | |
| Separated/Divorced | 55 (4.12%) | 361 (2.34%) | 85 (1.18%) | |
| Single | 1178 (88.2%) | 14212 (92.0%) | 2921 (40.6%) | |
| Widower | 1 (0.07%) | 24 (0.16%) | 5 (0.07%) | |
| Edad de Inicio de Consumo/Age of Onset of Drug Use | 14.0 [13.0;16.0] | 15.0 [13.0;16.0] | 14.9 [13.0;16.0] | <0.001 |
| Edad a la Fecha de Ingreso a Tratamiento (numérico continuo) (Primera Entrada)/Age at Admission to Treatment (First Entry) | 26.3 [23.5;28.3] | 24.7 [22.0;27.3] | 26.4 [24.1;28.3] | <0.001 |
| Sustancia de Inicio (Sólo más frecuentes)/Starting Substance (Only more frequent): | <0.001 | |||
| Alcohol | 634 (47.5%) | 7046 (45.6%) | 3532 (49.1%) | |
| Cocaine hydrochloride | 37 (2.77%) | 548 (3.55%) | 351 (4.88%) | |
| Cocaine paste | 68 (5.09%) | 637 (4.12%) | 353 (4.91%) | |
| Marijuana | 570 (42.7%) | 6974 (45.1%) | 2818 (39.2%) | |
| Other | 26 (1.95%) | 250 (1.62%) | 135 (1.88%) | |
| Starting Substance: | <0.001 | |||
| Alcohol | 628 (47.0%) | 6926 (44.8%) | 3499 (48.7%) | |
| Cocaine hydrochloride | 39 (2.92%) | 571 (3.69%) | 362 (5.04%) | |
| Marijuana | 573 (42.9%) | 7045 (45.6%) | 2834 (39.4%) | |
| Other | 24 (1.80%) | 245 (1.59%) | 129 (1.79%) | |
| Cocaine paste | 71 (5.32%) | 668 (4.32%) | 365 (5.08%) | |
| freq_cons_sus_prin: | <0.001 | |||
| 1 day a week or more | 55 (4.12%) | 904 (5.85%) | 657 (9.14%) | |
| 2 to 3 days a week | 331 (24.8%) | 4375 (28.3%) | 2422 (33.7%) | |
| 4 to 6 days a week | 204 (15.3%) | 2739 (17.7%) | 1165 (16.2%) | |
| Daily | 700 (52.4%) | 6804 (44.0%) | 2457 (34.2%) | |
| Less than 1 day a week | 45 (3.37%) | 633 (4.10%) | 488 (6.79%) | |
| Vía de Administración de la Sustancia Principal (Se aplicaron criterios de limpieza)(f)/Route of Administration of the Primary or Main Substance (Tidy)(f): | . | |||
| Smoked or Pulmonary Aspiration | 799 (59.9%) | 9269 (60.0%) | 3838 (53.4%) | |
| Intranasal (powder aspiration) | 205 (15.4%) | 3505 (22.7%) | 1865 (25.9%) | |
| Injected Intravenously or Intramuscularly | 2 (0.15%) | 12 (0.08%) | 2 (0.03%) | |
| Oral (drunk or eaten) | 328 (24.6%) | 2662 (17.2%) | 1480 (20.6%) | |
| Other | 1 (0.07%) | 7 (0.05%) | 4 (0.06%) | |
| Number of Children (Max. Value), adding 1 if pregnant at admission: | 0.000 | |||
| 0 | 586 (43.9%) | 8149 (52.7%) | 995 (13.8%) | |
| 1 | 463 (34.7%) | 4959 (32.1%) | 3242 (45.1%) | |
| 2 | 184 (13.8%) | 1724 (11.2%) | 2021 (28.1%) | |
| 3 | 70 (5.24%) | 477 (3.09%) | 671 (9.33%) | |
| 4 or more | 32 (2.40%) | 146 (0.94%) | 260 (3.62%) | |
| numero_de_hijos_mod_joel_bin: | 0.000 | |||
| 0 | 586 (43.9%) | 8149 (52.7%) | 995 (13.8%) | |
| One or more (or pregnant) | 749 (56.1%) | 7306 (47.3%) | 6194 (86.2%) | |
| Occupational Status Corrected(f): | <0.001 | |||
| Employed | 648 (48.5%) | 5601 (36.2%) | 4174 (58.1%) | |
| Inactive | 57 (4.27%) | 1231 (7.97%) | 731 (10.2%) | |
| Looking for a job for the first time | 3 (0.22%) | 78 (0.50%) | 16 (0.22%) | |
| No activity | 86 (6.44%) | 1015 (6.57%) | 232 (3.23%) | |
| Not seeking for work | 34 (2.55%) | 154 (1.00%) | 28 (0.39%) | |
| Unemployed | 507 (38.0%) | 7376 (47.7%) | 2008 (27.9%) | |
| Occupational Category Corrected(f): | . | |||
| Employer | 14 (1.05%) | 163 (1.05%) | 145 (2.02%) | |
| Other | 17 (1.27%) | 128 (0.83%) | 77 (1.07%) | |
| Salaried | 432 (32.4%) | 3833 (24.8%) | 2774 (38.6%) | |
| Self-employed | 119 (8.91%) | 953 (6.17%) | 862 (12.0%) | |
| Unpaid family labour | 1 (0.07%) | 52 (0.34%) | 12 (0.17%) | |
| Volunteer worker | 2 (0.15%) | 25 (0.16%) | 12 (0.17%) | |
| ‘Missing’ | 750 (56.2%) | 10301 (66.7%) | 3307 (46.0%) | |
| Abandono temprano(<3 meses)/ Early Drop-out(<3 months): | <0.001 | |||
| Mayor o igual a 90 días | 865 (64.8%) | 11229 (72.7%) | 5289 (73.6%) | |
| Menos de 90 días | 470 (35.2%) | 4226 (27.3%) | 1900 (26.4%) | |
| Diagnóstico CIE-10 (1 o más)(Recodificado)/Psychiatric Diagnoses (ICD-10)(one or more)(Recoded): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| With psychiatric comorbidity | 536 (40.1%) | 6415 (41.5%) | 2571 (35.8%) | |
| Días de Tratamiento (valores perdidos en la fecha de egreso se reemplazaron por la diferencia con 2019-11-13)/Days of Treatment (missing dates of discharge were replaced with difference from 2019-11-13) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Recuento de Diagnóstico de Trastorno Físico/Count of Physical Disorder | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.015 |
| Recuento de Otros Problemas de Atención Vinculados a Salud Mental/Count of Other problems linked to Mental Health | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | 0.00 [0.00;1.00] | <0.001 |
| Type of Plan (Independently of the Program): | <0.001 | |||
| PAB | 391 (29.3%) | 5510 (35.7%) | 3381 (47.0%) | |
| PAI | 587 (44.0%) | 7520 (48.7%) | 3300 (45.9%) | |
| PR | 357 (26.7%) | 2425 (15.7%) | 508 (7.07%) | |
| Tenure status of households: | 0.000 | |||
| Illegal Settlement | 97 (7.27%) | 48 (0.31%) | 90 (1.25%) | |
| Others | 78 (5.84%) | 374 (2.42%) | 114 (1.59%) | |
| Owner/Transferred dwellings/Pays Dividends | 307 (23.0%) | 5496 (35.6%) | 1956 (27.2%) | |
| Renting | 680 (50.9%) | 1155 (7.47%) | 2220 (30.9%) | |
| Stays temporarily with a relative | 173 (13.0%) | 8382 (54.2%) | 2809 (39.1%) | |
| Cum. Days of Treatment (1st Treatment) | 125 [68.0;224] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Cum. Days of Treatment (2nd Treatment) | 285 [174;423] | 320 [206;493] | 326 [218;489] | <0.001 |
| Cum. Days of Treatment (3rd Treatment) | 463 [260;644] | 474 [326;700] | 522 [361;730] | 0.008 |
| Cum. Days of Treatment (4th Treatment) | 526 [339;755] | 633 [426;878] | 671 [486;941] | 0.059 |
| Cum. Days of Treatment (5th Treatment) | 690 [532;1120] | 798 [541;1047] | 876 [576;1021] | 0.604 |
| Cum. Days of Treatment (6th Treatment) | 460 [356;565] | 897 [712;1153] | 1006 [868;1144] | 0.106 |
| Cum. Days of Treatment (7th Treatment) | 477 [376;578] | 1165 [993;1331] | 1076 [1074;1254] | 0.100 |
| Cum. Days of Treatment (8th Treatment) | . [.;.] | 1152 [1131;1172] | 1273 [1273;1273] | 0.221 |
| Cum. Days of Treatment (9th Treatment) | . [.;.] | 1403 [1403;1403] | . [.;.] | . |
| Cum. Days of Treatment (10th Treatment) | . [.;.] | 1622 [1622;1622] | . [.;.] | . |
| Days of Treatment (1st Treatment) | 125 [68.0;223] | 148 [84.0;257] | 147 [86.0;253] | <0.001 |
| Days of Treatment (2nd Treatment) | 119 [57.0;204] | 137 [76.0;239] | 139 [83.0;245] | <0.001 |
| Days of Treatment (3rd Treatment) | 127 [71.0;220] | 127 [70.0;229] | 148 [84.8;246] | 0.020 |
| Days of Treatment (4th Treatment) | 143 [57.2;248] | 125 [70.0;215] | 131 [71.0;231] | 0.997 |
| Days of Treatment (5th Treatment) | 199 [139;335] | 138 [67.8;254] | 145 [59.2;260] | 0.567 |
| Days of Treatment (6th Treatment) | 28.0 [17.5;38.5] | 149 [121;195] | 116 [68.5;205] | 0.121 |
| Days of Treatment (7th Treatment) | 16.5 [13.2;19.8] | 169 [58.5;244] | 53.0 [52.0;120] | 0.075 |
| Days of Treatment (8th Treatment) | . [.;.] | 84.5 [62.2;107] | 19.0 [19.0;19.0] | 0.221 |
| Days of Treatment (9th Treatment) | . [.;.] | 211 [211;211] | . [.;.] | . |
| Days of Treatment (10th Treatment) | . [.;.] | 219 [219;219] | . [.;.] | . |
| Cum. Diff Between Treatments (1st Treatment) | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.812 |
| Cum. Diff Between Treatments (2nd Treatment) | 715 [380;1315] | 779 [396;1355] | 872 [441;1492] | 0.075 |
| Cum. Diff Between Treatments (3rd Treatment) | 920 [405;1812] | 1128 [674;1648] | 1235 [770;1697] | 0.436 |
| Cum. Diff Between Treatments (4th Treatment) | 1290 [1044;2121] | 1196 [843;1722] | 1448 [1149;2190] | 0.034 |
| Cum. Diff Between Treatments (5th Treatment) | 2020 [1858;2182] | 1470 [1091;1768] | 1418 [944;2384] | 0.513 |
| Cum. Diff Between Treatments (6th Treatment) | 2536 [2415;2657] | 1576 [1244;2085] | 1221 [1015;1287] | 0.174 |
| Cum. Diff Between Treatments (7th Treatment) | . [.;.] | 1418 [1300;1537] | 1188 [1188;1188] | 1.000 |
| Cum. Diff Between Treatments (8th Treatment) | . [.;.] | 1706 [1706;1706] | . [.;.] | . |
| Cum. Diff Between Treatments (9th Treatment) | . [.;.] | 1944 [1944;1944] | . [.;.] | . |
| Cum. Diff Between Treatments (10th Treatment) | . | . | . | . |
| Número de Tratamientos por HASH (Total)/Number of Treatments by User (Total): | . | |||
| 1 | 1033 (77.4%) | 11903 (77.0%) | 5536 (77.0%) | |
| 2 | 207 (15.5%) | 2540 (16.4%) | 1161 (16.1%) | |
| 3 | 71 (5.32%) | 673 (4.35%) | 343 (4.77%) | |
| 4 | 15 (1.12%) | 233 (1.51%) | 99 (1.38%) | |
| 5 | 7 (0.52%) | 69 (0.45%) | 35 (0.49%) | |
| 6 | 0 (0.00%) | 27 (0.17%) | 10 (0.14%) | |
| 7 | 2 (0.15%) | 8 (0.05%) | 4 (0.06%) | |
| 8 | 0 (0.00%) | 1 (0.01%) | 1 (0.01%) | |
| 10 | 0 (0.00%) | 1 (0.01%) | 0 (0.00%) | |
| Max. Cumulative Days of Treatment | 154 [80.0;304] | 183 [98.0;332] | 186 [100;338] | <0.001 |
| Max. Cumulative Difference Between Treatments | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.00 [0.00;0.00] | 0.883 |
| Total count of Psychiatric & Drug dependence Diagnostics: | . | |||
| 0 | 224 (16.8%) | 2493 (16.1%) | 1547 (21.5%) | |
| 1 | 695 (52.1%) | 8175 (52.9%) | 3827 (53.2%) | |
| 2 | 399 (29.9%) | 4536 (29.3%) | 1742 (24.2%) | |
| 3 | 16 (1.20%) | 226 (1.46%) | 67 (0.93%) | |
| 4 | 1 (0.07%) | 25 (0.16%) | 6 (0.08%) | |
| Total count of Psychiatric Diagnostics: | . | |||
| 0 | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| 1 | 860 (64.4%) | 9441 (61.1%) | 4013 (55.8%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Conteo de Diagnósticos CIE-10(sólo diagnósticos)/Count of ICD-10 Diagnostics(only diagnoses): | . | |||
| 0 | 799 (59.9%) | 9040 (58.5%) | 4618 (64.2%) | |
| 1 | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| 2 | 19 (1.42%) | 264 (1.71%) | 82 (1.14%) | |
| 3 | 3 (0.22%) | 32 (0.21%) | 6 (0.08%) | |
| Days of Treatment (Fourth or those that follow) | 149 [51.8;234] | 138 [77.8;225] | 137 [85.4;236] | 0.966 |
| Days of Difference Between Treatments (Fifth treatment or those that folow) | 464 [238;588] | 258 [112;416] | 288 [164;682] | 0.064 |
| Cumulative Days of Treatment (Fourth or those that follow) | 590 [364;936] | 683 [454;932] | 727 [518;999] | 0.083 |
| Cumulative Difference Between Treatments (Fifth or those that follow) | 1760 [1044;2121] | 1345 [943;1759] | 1518 [1174;2252] | 0.081 |
| Average Cumulative Days of Treatment (Fourth or those that follow) | 133 [91.0;209] | 162 [107;218] | 171 [116;229] | 0.080 |
| Average Cumulative Difference Between Treatments (Fifth or those that follow) | 348 [261;497] | 312 [222;424] | 373 [286;544] | 0.064 |
| Comorbidity ICD-10 (with amount of different diagnosis): | <0.001 | |||
| Without psychiatric comorbidity | 453 (33.9%) | 5718 (37.0%) | 3088 (43.0%) | |
| Diagnosis unknown (under study) | 346 (25.9%) | 3322 (21.5%) | 1530 (21.3%) | |
| One | 514 (38.5%) | 6119 (39.6%) | 2483 (34.5%) | |
| Two or more | 22 (1.65%) | 296 (1.92%) | 88 (1.22%) | |
| No. of treatments with 18+ at admission between 2010 and 2019: | 0.426 | |||
| 01 | 1034 (77.5%) | 11905 (77.0%) | 5536 (77.0%) | |
| 02 | 206 (15.4%) | 2540 (16.4%) | 1161 (16.1%) | |
| 03 | 71 (5.32%) | 671 (4.34%) | 343 (4.77%) | |
| 04 or more | 24 (1.80%) | 339 (2.19%) | 149 (2.07%) | |
| Sexual abuse: | 0.034 | |||
| No sexual abuse | 1053 (78.9%) | 12207 (79.0%) | 5620 (78.2%) | |
| Sexual abuse | 27 (2.02%) | 175 (1.13%) | 87 (1.21%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Domestic violence: | <0.001 | |||
| No domestic violence | 745 (55.8%) | 9509 (61.5%) | 4050 (56.3%) | |
| Domestic violence | 335 (25.1%) | 2873 (18.6%) | 1657 (23.0%) | |
| ‘Missing’ | 255 (19.1%) | 3073 (19.9%) | 1482 (20.6%) | |
| Note. Variables of C1 dataset had to be standardized before comparison; | ||||
| Continuous variables are presented as Medians and Percentiles 25 and 75 were shown; | ||||
| Categorical variables are presented as number (%) |
Show code
#janitor::tabyl(prueba2_imp$numero_de_hijos_mod_joel)
Outcomes
Univariate or Bivariate Analyses
Here, we compared the incidence rate of those who experienced 1, 2, 3, 1 & 2, 1 to 3, 4 or more, and dropouts in the first treatment, between different “living with” patterns.
Show code
invisible("https://cran.r-project.org/web/packages/riskCommunicator/vignettes/Vignette_newbieRusers.html#continuous-exposure-example")
prueba2_imp2<-
cbind.data.frame(prueba2_imp,
dplyr::select(prueba2,cum_dias_trat_sin_na_1, cum_dias_trat_sin_na_2, cum_dias_trat_sin_na_3, cum_dias_trat_sin_na_4, cum_dias_trat_sin_na_5, cum_dias_trat_sin_na_6, cum_dias_trat_sin_na_7, cum_dias_trat_sin_na_8, cum_dias_trat_sin_na_9, cum_dias_trat_sin_na_10, dias_treat_imp_sin_na_1, dias_treat_imp_sin_na_2, dias_treat_imp_sin_na_3, dias_treat_imp_sin_na_4, dias_treat_imp_sin_na_5, dias_treat_imp_sin_na_6, dias_treat_imp_sin_na_7, dias_treat_imp_sin_na_8, dias_treat_imp_sin_na_9, dias_treat_imp_sin_na_10, cum_diff_bet_treat_1, cum_diff_bet_treat_2, cum_diff_bet_treat_3, cum_diff_bet_treat_4, cum_diff_bet_treat_5, cum_diff_bet_treat_6, cum_diff_bet_treat_7, cum_diff_bet_treat_8, cum_diff_bet_treat_9, cum_diff_bet_treat_10),
dplyr::select(prueba2, fech_ing, fech_egres_imp, sus_principal_mod, cat_ocupacional_corr, cnt_diagnostico_trs_fisico, cnt_otros_probl_at_sm_or, dg_cie_10_rec, abandono_temprano, duplicates_filtered, max_cum_dias_trat_sin_na, max_cum_diff_bet_treat, cnt_mod_cie_10_dg_cons_sus_or, cnt_mod_cie_10_or, dg_total_cie_10, dias_treat_imp_sin_na_four, diff_bet_treat_four, cum_dias_trat_sin_na_four, cum_diff_bet_treat_four, mean_cum_dias_trat_sin_na_four, mean_cum_diff_bet_treat_four, n_treats, sex_abuse, dom_violence, motivodeegreso_mod_imp)) %>%
dplyr::mutate(person_years=(as.numeric(as.Date("2019-11-13"))- as.numeric(fech_ing))/365.25,
#2023-02-02
event= dplyr::case_when(grepl("Drop-out|Administrative", motivodeegreso_mod_imp)~1, T~0),
yrs_to_tr_dropout= dplyr::case_when(event==1~ dias_treat_imp_sin_na_1/365.25, T~ (as.numeric(as.Date("2019-11-13"))- as.numeric(fech_ing))/365.25),
max_cum_dias_trat_sin_na_adj= max_cum_dias_trat_sin_na/person_years,
duplicates_filtered_adj= duplicates_filtered/person_years)#
#n_treats= readmissiones
tablas_inc<-
rbind(data.frame(biostat3::survRate(Surv(person_years, n_treats=="01") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, n_treats=="02") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, n_treats=="03") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, grepl("01|02",n_treats)) ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, grepl("01|02|03",n_treats)) ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, n_treats=="04 or more") ~ con_quien_vive_joel, data=prueba2_imp2)),
data.frame(biostat3::survRate(Surv(person_years, grepl("Drop", motivodeegreso_mod_imp)) ~ con_quien_vive_joel, data=prueba2_imp2)))
data.table::data.table(tablas_inc, keep.rownames = F)%>%
dplyr::mutate(rate=round(rate*1000,0),lower=round(lower*1000,0), upper=round(upper*1000,0))%>%
knitr::kable(format="html",caption= "Summary of incidence rates of events depending on whom patients live with")%>%
kableExtra::add_footnote(c("Note. Events per 1,000 person-years"), notation = "none")%>%
kableExtra::kable_classic() %>%
kableExtra::group_rows("Only one treatment",1,3)%>%
kableExtra::group_rows("Two treatments",4,6)%>%
kableExtra::group_rows("Three treatment",7,9)%>%
kableExtra::group_rows("One & two treatments",10,12)%>%
kableExtra::group_rows("One, two & three treatments",13,15)%>%
kableExtra::group_rows("Four or more treatments",16,18)%>%
kableExtra::group_rows("Drop-out from the first treatment",19,21)%>%
kableExtra::scroll_box(width = "100%", height = "600px")
| con_quien_vive_joel | tstop | event | rate | lower | upper |
|---|---|---|---|---|---|
| Only one treatment | |||||
| Alone | 6878.207 | 1034 | 150 | 141 | 160 |
| Family of origin | 78666.565 | 11905 | 151 | 149 | 154 |
| With couple/children | 37054.174 | 5536 | 149 | 145 | 153 |
| Two treatments | |||||
| Alone | 6878.207 | 206 | 30 | 26 | 34 |
| Family of origin | 78666.565 | 2540 | 32 | 31 | 34 |
| With couple/children | 37054.174 | 1161 | 31 | 30 | 33 |
| Three treatment | |||||
| Alone | 6878.207 | 71 | 10 | 8 | 13 |
| Family of origin | 78666.565 | 671 | 9 | 8 | 9 |
| With couple/children | 37054.174 | 343 | 9 | 8 | 10 |
| One & two treatments | |||||
| Alone | 6878.207 | 1240 | 180 | 170 | 191 |
| Family of origin | 78666.565 | 14445 | 184 | 181 | 187 |
| With couple/children | 37054.174 | 6697 | 181 | 176 | 185 |
| One, two & three treatments | |||||
| Alone | 6878.207 | 1311 | 191 | 180 | 201 |
| Family of origin | 78666.565 | 15116 | 192 | 189 | 195 |
| With couple/children | 37054.174 | 7040 | 190 | 186 | 194 |
| Four or more treatments | |||||
| Alone | 6878.207 | 24 | 3 | 2 | 5 |
| Family of origin | 78666.565 | 339 | 4 | 4 | 5 |
| With couple/children | 37054.174 | 149 | 4 | 3 | 5 |
| Drop-out from the first treatment | |||||
| Alone | 6878.207 | 766 | 111 | 104 | 120 |
| Family of origin | 78666.565 | 8691 | 110 | 108 | 113 |
| With couple/children | 37054.174 | 4417 | 119 | 116 | 123 |
| Note. Events per 1,000 person-years | |||||
The amount of treatments (duplicates_filtered) and the
maximum cumulative days spent in treatment
(max_cum_dias_trat_sin_na) by each user were adjusted by
dividing them by the years they were available in the study (we
subtracted the date of the first admission to the date of the end of the
follow-up).
Show code
rbind(
data.table::data.table(finalfit(prueba2_imp2,'max_cum_dias_trat_sin_na_adj', "con_quien_vive_joel"))[,2:5],
data.table::data.table(finalfit(prueba2_imp2,'duplicates_filtered_adj', "con_quien_vive_joel"))[,2:5], fill= T
) %>%
knitr::kable(format="html",caption= "Summary of adjusted measures, depending on whom patients live with") %>%
kableExtra::kable_classic() %>%
kableExtra::group_rows("Max. Cumulative Days of Treatment",1,3)%>%
kableExtra::group_rows("Number of Treatments by User (Total)",4,6)%>%
kableExtra::add_footnote(c("Note. Outcomes were adjusted for person-years", paste0("First regression= ", ff_metrics(lm(max_cum_dias_trat_sin_na_adj~ con_quien_vive_joel, prueba2_imp2))),paste0("Second regression= ",ff_metrics(lm(duplicates_filtered_adj~ con_quien_vive_joel, prueba2_imp2))
)), notation = "none")%>%
kableExtra::scroll_box(width = "100%", height = "350px")
| unit | value | Coefficient (univariable) | |
|---|---|---|---|
| Max. Cumulative Days of Treatment | |||
| Alone | Mean (sd) | 70.6 (93.5) |
|
| Family of origin | Mean (sd) | 80.1 (91.9) | 9.49 (4.44 to 14.55, p<0.001) |
| With couple/children | Mean (sd) | 75.9 (86.6) | 5.26 (-0.02 to 10.55, p=0.051) |
| Number of Treatments by User (Total) | |||
| Alone | Mean (sd) | 0.5 (1.1) |
|
| Family of origin | Mean (sd) | 0.6 (1.1) | 0.01 (-0.05 to 0.08, p=0.636) |
| With couple/children | Mean (sd) | 0.5 (1.0) | -0.03 (-0.10 to 0.03, p=0.323) |
| Note. Outcomes were adjusted for person-years | |||
| First regression= Number in dataframe = 23979, Number in model = 23979, Missing = 0, Log-likelihood = -142049.04, AIC = 284106.1, R-squared = 0.00087, Adjusted R-squared = 0.00079 | |||
| Second regression= Number in dataframe = 23979, Number in model = 23979, Missing = 0, Log-likelihood = -36286.27, AIC = 72580.5, R-squared = 0.00038, Adjusted R-squared = 0.00029 | |||
We checked the linear model with adjusted cumulative days of treatment
Show code
cat("Autocorrelation?")
Autocorrelation?Show code
lmtest::dwtest(prueba2_imp2$max_cum_dias_trat_sin_na_adj~ prueba2_imp2$con_quien_vive_joel)
Durbin-Watson test
data: prueba2_imp2$max_cum_dias_trat_sin_na_adj ~ prueba2_imp2$con_quien_vive_joel
DW = 1.9797, p-value = 0.0583
alternative hypothesis: true autocorrelation is greater than 0Show code
cat("Pattern in residuals?")
Pattern in residuals?Show code
Show code
cat("Are variances equal?")
Are variances equal?Show code
car::leveneTest(prueba2_imp2$max_cum_dias_trat_sin_na_adj,as.factor(prueba2_imp2$con_quien_vive_joel), center = mean)
Levene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 2 16.816 5.037e-08 ***
23976
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Show code
#Como el estadístico de Levene arroja un p-valor inferior a 0,05
#(Pr>F = 0,023) se rechaza la hipótesis nula de igualdad de varianzas.
mod<-aov(prueba2_imp2$max_cum_dias_trat_sin_na_adj~as.factor(prueba2_imp2$con_quien_vive_joel))
contraste <- rbind(c(1,1,-2),c(1,-1,0)) #
#Se entrega al programa los contrastes a utilizar:
filas<-c("Alone & Family of origin vs With couple/children","Alone vs Family of origin")
columnas<-c("Alone","Family of origin","With couple/children")
# contraste se definen un vector de números de acuerdo a las hipótesis formuladas
# función glht que tiene como argumento el modelo ANOVA definido como mod,
# y definir la variable grupal estado_nutricional= contraste. Por último, realizar un summary.
dimnames(contraste)<-list(filas,columnas)
cat("GLHT: User-defined General linear hypothesis")
GLHT: User-defined General linear hypothesisShow code
Simultaneous Tests for General Linear Hypotheses
Multiple Comparisons of Means: User-defined Contrasts
Fit: aov(formula = prueba2_imp2$max_cum_dias_trat_sin_na_adj ~ as.factor(prueba2_imp2$con_quien_vive_joel))
Linear Hypotheses:
Estimate
Alone & Family of origin vs With couple/children == 0 -1.033
Alone vs Family of origin == 0 -9.494
Std. Error
Alone & Family of origin vs With couple/children == 0 3.349
Alone vs Family of origin == 0 2.581
t value
Alone & Family of origin vs With couple/children == 0 -0.308
Alone vs Family of origin == 0 -3.679
Pr(>|t|)
Alone & Family of origin vs With couple/children == 0 0.924666
Alone vs Family of origin == 0 0.000453 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)Show code
cat("Not assuming equal variances")
Not assuming equal variancesShow code
oneway.test(max_cum_dias_trat_sin_na_adj~as.factor(con_quien_vive_joel),
data = prueba2_imp2,
var.equal=FALSE)
One-way analysis of means (not assuming equal variances)
data: max_cum_dias_trat_sin_na_adj and as.factor(con_quien_vive_joel)
F = 10.3, num df = 2.0, denom df = 3573.5, p-value = 3.463e-05Show code
cat("Tukey HSD")
Tukey HSDShow code
TukeyHSD(mod)
Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = prueba2_imp2$max_cum_dias_trat_sin_na_adj ~ as.factor(prueba2_imp2$con_quien_vive_joel))
$`as.factor(prueba2_imp2$con_quien_vive_joel)`
diff lwr upr
Family of origin-Alone 9.494373 3.445382 15.543364
With couple/children-Alone 5.263452 -1.056002 11.582906
With couple/children-Family of origin -4.230921 -7.258117 -1.203725
p adj
Family of origin-Alone 0.0006866
With couple/children-Alone 0.1243273
With couple/children-Family of origin 0.0030289Show code
cat("Games-Howell")
Games-HowellShow code
posthocTGH(prueba2_imp2$con_quien_vive_joel,
y = prueba2_imp2$max_cum_dias_trat_sin_na_adj, method= "games-howell", digits=4)
n means variances
Alone 1335 70.63 8749
Family of origin 15455 80.12 8454
With couple/children 7189 75.89 7501
diff ci.lo ci.hi t
Family of origin-Alone 9.494 3.243 15.746 3.563
With couple/children-Alone 5.263 -1.202 11.729 1.910
With couple/children-Family of origin -4.231 -7.187 -1.275 3.355
df p
Family of origin-Alone 1565 .0011
With couple/children-Alone 1784 .1362
With couple/children-Family of origin 14807 .0023Show code
#https://www.usabart.nl/eval/5-2%20ANOVA.pdf
Survival Analyses (Feb 2023)
Explore
Show code
#make dummy for each treatment arm
prueba2_imp2$con_quien_vive_joel_fam_or<-as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin")
prueba2_imp2$con_quien_vive_joel_alone<-as.numeric(prueba2_imp2$con_quien_vive_joel=="Alone")
prueba2_imp2$con_quien_vive_joel_coup_chil<-as.numeric(prueba2_imp2$con_quien_vive_joel=="With couple/children")
biostat3::survRate(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel, data= prueba2_imp2) %>%
#dplyr::mutate(rate=round(rate*1000,0),lower=round(lower*1000,0), upper=round(upper*1000,0))%>%
knitr::kable("markdown", caption= "Glimpse of the survival analysis, Years to drop-out in the First Treatment by Living Conditions")
| con_quien_vive_joel | tstop | event | rate | lower | upper | |
|---|---|---|---|---|---|---|
| con_quien_vive_joel=Alone | Alone | 2182.976 | 926 | 0.4241916 | 0.3973065 | 0.4524172 |
| con_quien_vive_joel=Family of origin | Family of origin | 27734.735 | 10209 | 0.3680944 | 0.3609883 | 0.3753052 |
| con_quien_vive_joel=With couple/children | With couple/children | 11721.960 | 5076 | 0.4330334 | 0.4212017 | 0.4451131 |
Show code
dropout_fit<- survfit(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel, data= prueba2_imp2,
type = "kaplan-meier",
error = "greenwood",
conf.type = "log-log")
survminer::ggsurvplot(dropout_fit,
#fun = "cumhaz",
conf.int = TRUE,
legend.labs = c("Alone", "Family of origin", "With couple/children"),
risk.table = "abs_pct",
#ncensor.plot = TRUE,
ggtheme = theme_classic(base_size=15),
risk.table.y.text.col = F,
risk.table.col="black",
font.tickslab = c(10),
risk.table.height = .2,
risk.table.fontsize = 2.5,
#break.time.by = 365.25,
pval = T,
#ylim=c(0,10),
legend = c(0.75, 0.8),
legend.title="Living conditions",
xlab= "Time (in years)",
#cumevents=T,
surv.connect = T,
censor= F
)
Figure 6: Kaplan-Meier curves by Living conditions
We calculated the restricted mean survival time and lost, considering that proportionality of trends could be violated.
Show code
cat("Living with Family of origin vs. Alone and With couple/children at 1 year")
Living with Family of origin vs. Alone and With couple/children at 1 yearShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin"), tau=1)
The truncation time: tau = 1 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 0.599 0.003 0.593 0.604
RMST (arm=0) 0.575 0.004 0.568 0.583
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 0.401 0.003 0.396 0.407
RMTL (arm=0) 0.425 0.004 0.417 0.432
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.024 0.014 0.033 0
RMST (arm=1)/(arm=0) 1.041 1.024 1.058 0
RMTL (arm=1)/(arm=0) 0.945 0.924 0.966 0Show code
cat("Living with Family of origin vs. Alone and With couple/children at 3 years")
Living with Family of origin vs. Alone and With couple/children at 3 yearsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin"), tau=3)
The truncation time: tau = 3 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 1.244 0.010 1.224 1.264
RMST (arm=0) 1.134 0.013 1.108 1.159
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 1.756 0.010 1.736 1.776
RMTL (arm=0) 1.866 0.013 1.841 1.892
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.110 0.078 0.143 0
RMST (arm=1)/(arm=0) 1.097 1.068 1.128 0
RMTL (arm=1)/(arm=0) 0.941 0.924 0.958 0Show code
cat("Living with Family of origin vs. Alone and With couple/children at 5 years")
Living with Family of origin vs. Alone and With couple/children at 5 yearsShow code
survRM2::rmst2(prueba2_imp2$yrs_to_tr_dropout, prueba2_imp2$event, as.numeric(prueba2_imp2$con_quien_vive_joel=="Family of origin"), tau=5)
The truncation time: tau = 5 was specified.
Restricted Mean Survival Time (RMST) by arm
Est. se lower .95 upper .95
RMST (arm=1) 1.865 0.018 1.831 1.900
RMST (arm=0) 1.671 0.023 1.627 1.716
Restricted Mean Time Lost (RMTL) by arm
Est. se lower .95 upper .95
RMTL (arm=1) 3.135 0.018 3.100 3.169
RMTL (arm=0) 3.329 0.023 3.284 3.373
Between-group contrast
Est. lower .95 upper .95 p
RMST (arm=1)-(arm=0) 0.194 0.137 0.250 0
RMST (arm=1)/(arm=0) 1.116 1.080 1.153 0
RMTL (arm=1)/(arm=0) 0.942 0.926 0.958 0Show code
#https://rpubs.com/linpearl89/TTE-RCT
#https://search.r-project.org/CRAN/refmans/adjustedCurves/html/surv_aiptw_pseudo.html
#https://search.r-project.org/CRAN/refmans/adjustedCurves/html/surv_iptw_cox.html
#https://cran.r-project.org/web/packages/RISCA/RISCA.pdf
coxfit <- coxph(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel + edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, ties="efron", data= prueba2_imp2)
survminer::ggcoxdiagnostics(coxfit, type = "schoenfeld")
Figure 7: Schoefeld residuals
Show code
ggsave("prueba_scho_plot.png", dpi=640, height=15, width= 15)
Show code
data.frame(cox.zph(coxfit)$table) %>%
knitr::kable("markdown", caption= "Schoefeld residuals")
| chisq | df | p | |
|---|---|---|---|
| con_quien_vive_joel | 64.692554 | 2 | 0.0000000 |
| edad_al_ing | 39.352315 | 1 | 0.0000000 |
| sexo_2 | 61.242208 | 1 | 0.0000000 |
| escolaridad_rec | 4.948839 | 2 | 0.0842119 |
| sus_ini_mod_mvv | 33.322975 | 4 | 0.0000010 |
| freq_cons_sus_prin | 190.059855 | 4 | 0.0000000 |
| via_adm_sus_prin_act | 45.900752 | 4 | 0.0000000 |
| condicion_ocupacional_corr | 225.182444 | 5 | 0.0000000 |
| compromiso_biopsicosocial | 137.029722 | 2 | 0.0000000 |
| numero_de_hijos_mod_joel | 27.132276 | 4 | 0.0000187 |
| tipo_de_plan_2_mod | 705.936689 | 2 | 0.0000000 |
| tenencia_de_la_vivienda_mod | 44.136105 | 4 | 0.0000000 |
| tipo_centro | 574.013144 | 1 | 0.0000000 |
| cnt_mod_cie_10_dg_cons_sus_or | 105.519418 | 1 | 0.0000000 |
| sus_principal_mod | 62.813227 | 4 | 0.0000000 |
| macrozona | 132.264481 | 2 | 0.0000000 |
| cnt_mod_cie_10_or | 690.849027 | 3 | 0.0000000 |
| GLOBAL | 2822.282319 | 46 | 0.0000000 |
Show code
#If the value of VIF is less than 1: no correlation - If the value of VIF is between 1-5, there is moderate correlation - If the value of VIF is above 5: severe correlation
#_#_#_#_#_#_#_#_#_
invisible("3.Cox, collinearity")
#global covs_3 "i.caus_disch_mod_imp_rec edad_al_ing_1 edad_ini_cons i.sex_enc i.esc_rec i.sus_prin_mod i.fr_sus_prin i.comp_biosoc i.ten_viv i.dg_cie_10_rec i.sud_severity_icd10 i.macrozone i.policonsumo i.n_off_vio i.n_off_acq i.n_off_sud "
#Error in Design(data, formula, specials = c("strat", "strata")) :
# Variable escolaridad_rec is an ordered factor with non-numeric levels.
# You should set options(contrasts=c("contr.treatment", "contr.treatment"))
#or rms will not work properly.
options(contrasts=c("contr.treatment", "contr.treatment"))
#https://randomeffect.net/post/2021/05/02/the-rms-validate-function/
f1 <- cph(Surv(yrs_to_tr_dropout, event) ~ con_quien_vive_joel + edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, data= prueba2_imp2, x=T, y=T)
#warning("X matrix deemed to be singular; variable n_off_oth")
cvif <- rms::vif(f1)
cvif%>%
knitr::kable("markdown", caption= "Variance Inflation Factors in Cox Regressions")
| x | |
|---|---|
| con_quien_vive_joel=Family of origin | 5.062774 |
| con_quien_vive_joel=With couple/children | 4.815530 |
| edad_al_ing | 1.185765 |
| sexo_2=Women | 1.279537 |
| escolaridad_rec=2-Completed high school or less | 1.390392 |
| escolaridad_rec=1-More than high school | 1.446157 |
| sus_ini_mod_mvv=Cocaine hydrochloride | 1.116117 |
| sus_ini_mod_mvv=Marijuana | 1.308500 |
| sus_ini_mod_mvv=Other | 1.066281 |
| sus_ini_mod_mvv=Cocaine paste | 1.149405 |
| freq_cons_sus_prin=2 to 3 days a week | 3.982403 |
| freq_cons_sus_prin=4 to 6 days a week | 3.152015 |
| freq_cons_sus_prin=Daily | 4.767005 |
| freq_cons_sus_prin=Less than 1 day a week | 1.663196 |
| via_adm_sus_prin_act=Intranasal (powder aspiration) | 23.948588 |
| via_adm_sus_prin_act=Injected Intravenously or Intramuscularly | 1.093445 |
| via_adm_sus_prin_act=Oral (drunk or eaten) | 22.726709 |
| via_adm_sus_prin_act=Other | 1.009243 |
| condicion_ocupacional_corr=Inactive | 1.255954 |
| condicion_ocupacional_corr=Looking for a job for the first time | 1.011687 |
| condicion_ocupacional_corr=No activity | 1.152128 |
| condicion_ocupacional_corr=Not seeking for work | 1.029332 |
| condicion_ocupacional_corr=Unemployed | 1.397809 |
| compromiso_biopsicosocial=2-Moderate | 3.358003 |
| compromiso_biopsicosocial=3-Severe | 4.075207 |
| numero_de_hijos_mod_joel=1 | 1.435555 |
| numero_de_hijos_mod_joel=2 | 1.496315 |
| numero_de_hijos_mod_joel=3 | 1.241426 |
| numero_de_hijos_mod_joel=4 or more | 1.137270 |
| tipo_de_plan_2_mod=PAI | 1.490520 |
| tipo_de_plan_2_mod=PR | 1.935490 |
| tenencia_de_la_vivienda_mod=Others | 3.351341 |
| tenencia_de_la_vivienda_mod=Owner/Transferred dwellings/Pays Dividends | 22.357871 |
| tenencia_de_la_vivienda_mod=Renting | 14.819127 |
| tenencia_de_la_vivienda_mod=Stays temporarily with a relative | 25.766616 |
| tipo_centro=Public | 1.387114 |
| cnt_mod_cie_10_dg_cons_sus_or | 1.801449 |
| sus_principal_mod=Cocaine hydrochloride | 50.324496 |
| sus_principal_mod=Marijuana | 17.214365 |
| sus_principal_mod=Other | 1.210444 |
| sus_principal_mod=Cocaine paste | 41.689593 |
| macrozona=North | 1.183885 |
| macrozona=South | 1.107565 |
| cnt_mod_cie_10_or | 1.637842 |
| cnt_mod_cie_10_or=2 | 1.125584 |
| cnt_mod_cie_10_or=3 | 1.037060 |
Show code
# dplyr::mutate_if(is.numeric,~round(.,2)) %>%
# DT::datatable()
Adjusted
We calculated the restricted mean survival time and lost, considering that proportionality of trends could be violated.
Show code
#(3) (PDF) Adjusted restricted mean survival times in observational studies. Available from: https://www.researchgate.net/publication/333326572_Adjusted_restricted_mean_survival_times_in_observational_studies [accessed Feb 02 2023].
logit <- glm (con_quien_vive_joel_fam_or ~ edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or,
data=prueba2_imp2, family=binomial(link= "logit"))
pred <- predict(logit, type="response")
prueba2_imp2$weight <- prueba2_imp2$con_quien_vive_joel_fam_or/pred + (1 - prueba2_imp2$con_quien_vive_joel_fam_or)/(1-pred)
#https://anthonyongphd.files.wordpress.com/2016/02/emerging-adulthood-2016-thoemmes-40-59.pdf
#A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
#truncate weights at 1%
prueba2_imp2$weight_tr <- ifelse(prueba2_imp2$weight < quantile(prueba2_imp2$weight, probs=.01), quantile(prueba2_imp2$weight, probs=.01), prueba2_imp2$weight)
prueba2_imp2$weight_tr <- ifelse(prueba2_imp2$weight > quantile(prueba2_imp2$weight, probs=.99), quantile(prueba2_imp2$weight, probs=.99), prueba2_imp2$weight_tr)
#truncate weights at 5%
prueba2_imp2$weight_tr2 <- ifelse(prueba2_imp2$weight < quantile(prueba2_imp2$weight, probs=.05), quantile(prueba2_imp2$weight, probs=.05), prueba2_imp2$weight)
prueba2_imp2$weight_tr2 <- ifelse(prueba2_imp2$weight > quantile(prueba2_imp2$weight, probs=.95), quantile(prueba2_imp2$weight, probs=.95), prueba2_imp2$weight_tr2)
#truncate weights at 5%
prueba2_imp2$weight_tr3 <- ifelse(prueba2_imp2$weight < quantile(prueba2_imp2$weight, probs=.1), quantile(prueba2_imp2$weight, probs=.1), prueba2_imp2$weight)
prueba2_imp2$weight_tr3 <- ifelse(prueba2_imp2$weight > quantile(prueba2_imp2$weight, probs=.9), quantile(prueba2_imp2$weight, probs=.9), prueba2_imp2$weight_tr3)
cat("===============================================================================")
===============================================================================Show code
logit2 <- glm (con_quien_vive_joel_alone ~ edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or,
data=prueba2_imp2, family=binomial(link= "logit"))
pred2 <- predict(logit2, type="response")
prueba2_imp2$weight_a <- prueba2_imp2$con_quien_vive_joel_alone/pred2 + (1 - prueba2_imp2$con_quien_vive_joel_alone)/(1-pred2)
#https://anthonyongphd.files.wordpress.com/2016/02/emerging-adulthood-2016-thoemmes-40-59.pdf
#A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
#truncate weights at 1%
prueba2_imp2$weight_tr_a <- ifelse(prueba2_imp2$weight_a < quantile(prueba2_imp2$weight_a, probs=.01), quantile(prueba2_imp2$weight_a, probs=.01), prueba2_imp2$weight_a)
prueba2_imp2$weight_tr_a <- ifelse(prueba2_imp2$weight_a > quantile(prueba2_imp2$weight_a, probs=.99), quantile(prueba2_imp2$weight_a, probs=.99), prueba2_imp2$weight_tr_a)
#truncate weights at 5%
prueba2_imp2$weight_tr2_a <- ifelse(prueba2_imp2$weight_a < quantile(prueba2_imp2$weight_a, probs=.05), quantile(prueba2_imp2$weight, probs=.05), prueba2_imp2$weight)
prueba2_imp2$weight_tr2_a <- ifelse(prueba2_imp2$weight_a > quantile(prueba2_imp2$weight_a, probs=.95), quantile(prueba2_imp2$weight_a, probs=.95), prueba2_imp2$weight_tr2_a)
#truncate weights at 5%
prueba2_imp2$weight_tr3_a <- ifelse(prueba2_imp2$weight_a < quantile(prueba2_imp2$weight_a, probs=.1), quantile(prueba2_imp2$weight_a, probs=.1), prueba2_imp2$weight_a)
prueba2_imp2$weight_tr3_a <- ifelse(prueba2_imp2$weight_a > quantile(prueba2_imp2$weight_a, probs=.9), quantile(prueba2_imp2$weight_a, probs=.9), prueba2_imp2$weight_tr3_a)
cat("===============================================================================")
===============================================================================Show code
logit3 <- glm (con_quien_vive_joel_coup_chil ~ edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or,
data=prueba2_imp2, family=binomial(link= "logit"))
pred3 <- predict(logit3, type="response")
prueba2_imp2$weight_b <- prueba2_imp2$con_quien_vive_joel_coup_chil/pred + (1 - prueba2_imp2$con_quien_vive_joel_coup_chil)/(1-pred)
#https://anthonyongphd.files.wordpress.com/2016/02/emerging-adulthood-2016-thoemmes-40-59.pdf
#A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models
#truncate weights at 1%
prueba2_imp2$weight_tr_b <- ifelse(prueba2_imp2$weight_b < quantile(prueba2_imp2$weight_b, probs=.01), quantile(prueba2_imp2$weight_b, probs=.01), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr_b <- ifelse(prueba2_imp2$weight_b > quantile(prueba2_imp2$weight_b, probs=.99), quantile(prueba2_imp2$weight_b, probs=.99), prueba2_imp2$weight_tr_b)
#truncate weights at 5%
prueba2_imp2$weight_tr2_b <- ifelse(prueba2_imp2$weight_b < quantile(prueba2_imp2$weight_b, probs=.05), quantile(prueba2_imp2$weight_b, probs=.05), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr2_b <- ifelse(prueba2_imp2$weight_b > quantile(prueba2_imp2$weight_b, probs=.95), quantile(prueba2_imp2$weight_b, probs=.95), prueba2_imp2$weight_tr2_b)
#truncate weights at 5%
prueba2_imp2$weight_tr3_b <- ifelse(prueba2_imp2$weight_b < quantile(prueba2_imp2$weight_b, probs=.1), quantile(prueba2_imp2$weight_b, probs=.1), prueba2_imp2$weight_b)
prueba2_imp2$weight_tr3_b <- ifelse(prueba2_imp2$weight_b > quantile(prueba2_imp2$weight_b, probs=.9), quantile(prueba2_imp2$weight_b, probs=.9), prueba2_imp2$weight_tr3_b)
# AKM RMST adjusted for age
source("https://raw.githubusercontent.com/s-conner/akm-rmst/master/AKM_rmst.R")
cat("===============================================================================")
===============================================================================Show code
cat("Living with Family of origin(1) vs. Alone and With couple/children at 1 year (adjusted)")
Living with Family of origin(1) vs. Alone and With couple/children at 1 year (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 0.571 0.005
Group 1 0.593 0.003
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 0.023 0.006 0.011 0.035 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 0.039 0.01 1.04 1.019 1.062 0Show code
cat("Living with Family of origin(1) vs. Alone and With couple/children at 3 years (adjusted)")
Living with Family of origin(1) vs. Alone and With couple/children at 3 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.141 0.017
Group 1 1.220 0.012
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 0.079 0.021 0.038 0.12 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 0.067 0.018 1.069 1.032 1.108 0Show code
cat("Living with Family of origin(1) vs. Alone and With couple/children at 5 years (adjusted)")
Living with Family of origin(1) vs. Alone and With couple/children at 5 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr, tau=5)
RMST calculated up to tau = 5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.694 0.030
Group 1 1.822 0.021
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 0.129 0.037 0.057 0.2 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 0.073 0.021 1.076 1.032 1.121 0.001Show code
cat("===============================================================================")
===============================================================================Show code
cat("Living Alone(1) vs. with Family of origin and With couple/children at 1 year (adjusted)")
Living Alone(1) vs. with Family of origin and With couple/children at 1 year (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr_a, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 0.592 0.002
Group 1 0.560 0.013
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.032 0.013 -0.058 -0.006 0.015
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.056 0.024 0.945 0.903 0.99 0.017Show code
cat("Living Alone(1) vs. with Family of origin and With couple/children at 3 years (adjusted)")
Living Alone(1) vs. with Family of origin and With couple/children at 3 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr_a, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.209 0.008
Group 1 1.131 0.044
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.078 0.045 -0.167 0.01 0.082
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.067 0.04 0.935 0.865 1.011 0.092Show code
cat("Living Alone(1) vs. with Family of origin and With couple/children at 5 years (adjusted)")
Living Alone(1) vs. with Family of origin and With couple/children at 5 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr_a, tau=5)
RMST calculated up to tau = 5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.804 0.014
Group 1 1.689 0.077
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.115 0.079 -0.27 0.039 0.143
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.066 0.047 0.936 0.855 1.026 0.156Show code
cat("===============================================================================")
===============================================================================Show code
cat("Living with couple/children(1) vs. Family of origin & alone at 1 year (adjusted)")
Living with couple/children(1) vs. Family of origin & alone at 1 year (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr_b, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 0.605 0.004
Group 1 0.589 0.006
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.016 0.007 -0.03 -0.003 0.017
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.027 0.012 0.973 0.951 0.995 0.018Show code
cat("Living with couple/children(1) vs. Family of origin & alone at 3 years (adjusted)")
Living with couple/children(1) vs. Family of origin & alone at 3 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr_b, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.296 0.013
Group 1 1.144 0.020
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.152 0.024 -0.199 -0.105 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.125 0.02 0.883 0.848 0.918 0Show code
cat("Living with couple/children(1) vs. Family of origin & alone at 5 years (adjusted)")
Living with couple/children(1) vs. Family of origin & alone at 5 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr_b, tau=5)
RMST calculated up to tau = 5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.965 0.023
Group 1 1.674 0.035
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.291 0.042 -0.373 -0.209 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.16 0.024 0.852 0.813 0.893 0We assess the covariates’ balance after the weighting
Show code
bal_tab<-
cobalt::bal.tab(event ~ con_quien_vive_joel_fam_or+ edad_al_ing+ sexo_2 + escolaridad_rec + sus_ini_mod_mvv + freq_cons_sus_prin + via_adm_sus_prin_act + condicion_ocupacional_corr+ compromiso_biopsicosocial + numero_de_hijos_mod_joel + tipo_de_plan_2_mod+ tenencia_de_la_vivienda_mod + tipo_centro+ cnt_mod_cie_10_dg_cons_sus_or + sus_principal_mod+ macrozona + cnt_mod_cie_10_or, data = prueba2_imp2,
weights = c("weight","weight_tr", "weight_tr2", "weight_tr3"),
estimand = "ATT",
un = T,
binary = "std", continuous = "std",
stats = c("mean.diffs", "variance.ratios"))
bal_tab$Balance %>%
dplyr::mutate_if(is.numeric, ~round(.,2)) %>%
knitr::kable("markdown", caption="Balance Measures")
| Type | Diff.Un | V.Ratio.Un | Diff.weight | V.Ratio.weight | Diff.weight_tr | V.Ratio.weight_tr | Diff.weight_tr2 | V.Ratio.weight_tr2 | Diff.weight_tr3 | V.Ratio.weight_tr3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| con_quien_vive_joel_fam_or | Binary | -0.09 | -0.06 | -0.06 | -0.07 | -0.08 | |||||
| edad_al_ing | Contin. | 0.00 | 0.94 | -0.02 | 0.96 | -0.02 | 0.96 | -0.02 | 0.96 | -0.02 | 0.95 |
| sexo_2_Women | Binary | -0.08 | -0.10 | -0.09 | -0.09 | -0.09 | |||||
| escolaridad_rec_3-Completed primary school or less | Binary | 0.15 | 0.16 | 0.16 | 0.16 | 0.15 | |||||
| escolaridad_rec_2-Completed high school or less | Binary | 0.01 | 0.02 | 0.01 | 0.01 | 0.00 | |||||
| escolaridad_rec_1-More than high school | Binary | -0.21 | -0.23 | -0.22 | -0.21 | -0.20 | |||||
| sus_ini_mod_mvv_Alcohol | Binary | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | |||||
| sus_ini_mod_mvv_Cocaine hydrochloride | Binary | 0.04 | 0.04 | 0.04 | 0.04 | 0.04 | |||||
| sus_ini_mod_mvv_Marijuana | Binary | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | |||||
| sus_ini_mod_mvv_Other | Binary | -0.01 | -0.01 | -0.01 | -0.02 | -0.02 | |||||
| sus_ini_mod_mvv_Cocaine paste | Binary | 0.06 | 0.06 | 0.07 | 0.07 | 0.07 | |||||
| freq_cons_sus_prin_1 day a week or more | Binary | -0.03 | -0.04 | -0.04 | -0.03 | -0.03 | |||||
| freq_cons_sus_prin_2 to 3 days a week | Binary | 0.02 | 0.03 | 0.03 | 0.02 | 0.02 | |||||
| freq_cons_sus_prin_4 to 6 days a week | Binary | 0.01 | 0.01 | 0.02 | 0.02 | 0.02 | |||||
| freq_cons_sus_prin_Daily | Binary | -0.01 | -0.01 | -0.02 | -0.02 | -0.01 | |||||
| freq_cons_sus_prin_Less than 1 day a week | Binary | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | |||||
| via_adm_sus_prin_act_Smoked or Pulmonary Aspiration | Binary | 0.11 | 0.13 | 0.12 | 0.12 | 0.12 | |||||
| via_adm_sus_prin_act_Intranasal (powder aspiration) | Binary | -0.01 | -0.02 | -0.02 | -0.02 | -0.02 | |||||
| via_adm_sus_prin_act_Injected Intravenously or Intramuscularly | Binary | -0.03 | -0.03 | -0.03 | -0.03 | -0.04 | |||||
| via_adm_sus_prin_act_Oral (drunk or eaten) | Binary | -0.13 | -0.13 | -0.14 | -0.13 | -0.14 | |||||
| via_adm_sus_prin_act_Other | Binary | -0.01 | -0.02 | -0.02 | -0.02 | -0.02 | |||||
| condicion_ocupacional_corr_Employed | Binary | 0.10 | 0.09 | 0.09 | 0.10 | 0.10 | |||||
| condicion_ocupacional_corr_Inactive | Binary | -0.09 | -0.09 | -0.09 | -0.09 | -0.09 | |||||
| condicion_ocupacional_corr_Looking for a job for the first time | Binary | -0.01 | -0.01 | -0.01 | -0.01 | -0.01 | |||||
| condicion_ocupacional_corr_No activity | Binary | -0.07 | -0.02 | -0.02 | -0.04 | -0.04 | |||||
| condicion_ocupacional_corr_Not seeking for work | Binary | -0.03 | -0.01 | -0.01 | -0.02 | -0.02 | |||||
| condicion_ocupacional_corr_Unemployed | Binary | -0.02 | -0.03 | -0.03 | -0.03 | -0.03 | |||||
| compromiso_biopsicosocial_1-Mild | Binary | -0.02 | -0.02 | -0.02 | -0.02 | -0.03 | |||||
| compromiso_biopsicosocial_2-Moderate | Binary | 0.02 | 0.01 | 0.02 | 0.03 | 0.03 | |||||
| compromiso_biopsicosocial_3-Severe | Binary | -0.01 | 0.00 | -0.01 | -0.01 | -0.01 | |||||
| numero_de_hijos_mod_joel_0 | Binary | -0.14 | -0.11 | -0.11 | -0.11 | -0.12 | |||||
| numero_de_hijos_mod_joel_1 | Binary | 0.09 | 0.06 | 0.06 | 0.06 | 0.07 | |||||
| numero_de_hijos_mod_joel_2 | Binary | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | |||||
| numero_de_hijos_mod_joel_3 | Binary | 0.01 | -0.01 | -0.01 | -0.01 | -0.01 | |||||
| numero_de_hijos_mod_joel_4 or more | Binary | 0.05 | 0.05 | 0.05 | 0.05 | 0.05 | |||||
| tipo_de_plan_2_mod_PAB | Binary | 0.15 | 0.16 | 0.16 | 0.16 | 0.16 | |||||
| tipo_de_plan_2_mod_PAI | Binary | -0.06 | -0.07 | -0.06 | -0.06 | -0.05 | |||||
| tipo_de_plan_2_mod_PR | Binary | -0.14 | -0.13 | -0.14 | -0.16 | -0.16 | |||||
| tenencia_de_la_vivienda_mod_Illegal Settlement | Binary | 0.02 | -0.01 | -0.01 | -0.01 | 0.00 | |||||
| tenencia_de_la_vivienda_mod_Others | Binary | 0.02 | 0.03 | 0.03 | 0.02 | 0.02 | |||||
| tenencia_de_la_vivienda_mod_Owner/Transferred dwellings/Pays Dividends | Binary | -0.06 | -0.05 | -0.05 | -0.04 | -0.04 | |||||
| tenencia_de_la_vivienda_mod_Renting | Binary | 0.01 | -0.01 | -0.02 | -0.02 | -0.02 | |||||
| tenencia_de_la_vivienda_mod_Stays temporarily with a relative | Binary | 0.04 | 0.05 | 0.05 | 0.05 | 0.05 | |||||
| tipo_centro_Public | Binary | 0.10 | 0.11 | 0.11 | 0.12 | 0.12 | |||||
| cnt_mod_cie_10_dg_cons_sus_or | Contin. | -0.17 | 0.89 | -0.15 | 0.89 | -0.16 | 0.89 | -0.16 | 0.89 | -0.15 | 0.89 |
| sus_principal_mod_Alcohol | Binary | -0.12 | -0.12 | -0.13 | -0.13 | -0.13 | |||||
| sus_principal_mod_Cocaine hydrochloride | Binary | -0.01 | -0.03 | -0.02 | -0.02 | -0.02 | |||||
| sus_principal_mod_Marijuana | Binary | -0.11 | -0.08 | -0.09 | -0.09 | -0.10 | |||||
| sus_principal_mod_Other | Binary | -0.08 | -0.07 | -0.06 | -0.06 | -0.07 | |||||
| sus_principal_mod_Cocaine paste | Binary | 0.19 | 0.18 | 0.18 | 0.18 | 0.18 | |||||
| macrozona_Center | Binary | 0.15 | 0.14 | 0.14 | 0.15 | 0.15 | |||||
| macrozona_North | Binary | -0.01 | -0.01 | -0.01 | -0.02 | -0.02 | |||||
| macrozona_South | Binary | -0.23 | -0.22 | -0.22 | -0.21 | -0.22 | |||||
| cnt_mod_cie_10_or_0 | Binary | -0.06 | -0.11 | -0.10 | -0.09 | -0.09 | |||||
| cnt_mod_cie_10_or_1 | Binary | 0.08 | 0.13 | 0.13 | 0.11 | 0.10 | |||||
| cnt_mod_cie_10_or_2 | Binary | -0.07 | -0.09 | -0.08 | -0.08 | -0.07 | |||||
| cnt_mod_cie_10_or_3 | Binary | -0.04 | -0.05 | -0.05 | -0.05 | -0.04 |
Show code
bal_tab$Observations %>%
#dplyr::mutate_if(is.numeric, ~round(.,2)) %>%
knitr::kable("markdown", caption="Effective sample sizes")
| Control | Treated | |
|---|---|---|
| All | 7768.000 | 16211.000 |
| weight | 3788.064 | 8173.960 |
| weight_tr | 4324.160 | 9312.919 |
| weight_tr2 | 5886.040 | 12474.737 |
| weight_tr3 | 6652.756 | 13990.583 |
Educational attainment in “More than high school” category had great differences between groups. Also the South Macrozone showed high standardized differences, meaning that the sample could not be balanced yet. Truncating at 5% and 10% behaves better. We decided to use the most truncated weight
Show code
#(3) (PDF) Adjusted restricted mean survival times in observational studies. Available from: https://www.researchgate.net/publication/333326572_Adjusted_restricted_mean_survival_times_in_observational_studies [accessed Feb 02 2023].
# AKM RMST adjusted for age
source("https://raw.githubusercontent.com/s-conner/akm-rmst/master/AKM_rmst.R")
cat("Living with Family of origin(1) vs. Alone and With couple/children at 1 year (adjusted)")
Living with Family of origin(1) vs. Alone and With couple/children at 1 year (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr3, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 0.573 0.004
Group 1 0.595 0.003
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 0.022 0.005 0.012 0.032 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 0.037 0.009 1.038 1.021 1.056 0Show code
cat("Living with Family of origin(1) vs. Alone and With couple/children at 3 years (adjusted)")
Living with Family of origin(1) vs. Alone and With couple/children at 3 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr3, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.134 0.014
Group 1 1.223 0.011
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 0.09 0.017 0.056 0.124 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 0.076 0.015 1.079 1.048 1.111 0Show code
cat("Living with Family of origin(1) vs. Alone and With couple/children at 5 years (adjusted)")
Living with Family of origin(1) vs. Alone and With couple/children at 5 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_fam_or),weight=prueba2_imp2$weight_tr3, tau=5)
RMST calculated up to tau = 5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.675 0.024
Group 1 1.828 0.018
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 0.153 0.03 0.093 0.212 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 0.087 0.018 1.091 1.054 1.13 0Show code
cat("===============================================================================")
===============================================================================Show code
cat("Living Alone(1) vs. with Family of origin and With couple/children at 1 year (adjusted)")
Living Alone(1) vs. with Family of origin and With couple/children at 1 year (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr3, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 0.588 0.003
Group 1 0.550 0.011
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.038 0.011 -0.06 -0.016 0.001
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.067 0.02 0.935 0.899 0.973 0.001Show code
cat("Living Alone(1) vs. with Family of origin and With couple/children at 3 years (adjusted)")
Living Alone(1) vs. with Family of origin and With couple/children at 3 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr3, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.187 0.009
Group 1 1.141 0.037
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.046 0.038 -0.12 0.028 0.226
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.039 0.033 0.962 0.901 1.026 0.234Show code
cat("Living Alone(1) vs. with Family of origin and With couple/children at 5 years (adjusted)")
Living Alone(1) vs. with Family of origin and With couple/children at 5 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_alone),weight=prueba2_imp2$weight_tr3, tau=5)
RMST calculated up to tau = 5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.764 0.015
Group 1 1.720 0.064
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.043 0.066 -0.172 0.085 0.508
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.025 0.038 0.975 0.905 1.051 0.513Show code
cat("===============================================================================")
===============================================================================Show code
cat("Living with couple/children(1) vs. Family of origin & alone at 1 year (adjusted)")
Living with couple/children(1) vs. Family of origin & alone at 1 year (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr3, tau=1)
RMST calculated up to tau = 1
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 0.590 0.003
Group 1 0.577 0.004
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.013 0.005 -0.023 -0.002 0.016
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.022 0.009 0.979 0.961 0.996 0.017Show code
cat("Living with couple/children(1) vs. Family of origin & alone at 3 years (adjusted)")
Living with couple/children(1) vs. Family of origin & alone at 3 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr3, tau=3)
RMST calculated up to tau = 3
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.214 0.010
Group 1 1.132 0.015
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.082 0.018 -0.118 -0.047 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.07 0.016 0.932 0.904 0.961 0Show code
cat("Living with couple/children(1) vs. Family of origin & alone at 5 years (adjusted)")
Living with couple/children(1) vs. Family of origin & alone at 5 years (adjusted)Show code
akm_rmst(time=prueba2_imp2$yrs_to_tr_dropout, status=prueba2_imp2$event, group=as.factor(prueba2_imp2$con_quien_vive_joel_coup_chil),weight=prueba2_imp2$weight_tr3, tau=5)
RMST calculated up to tau = 5
Restricted Mean Survival Time (RMST) per Group
RMST SE
Group 0 1.816 0.018
Group 1 1.667 0.026
Restricted Mean Survival Time (RMST) Differences
Est. SE CIL CIU p
Groups 1 vs. 0 -0.149 0.032 -0.212 -0.087 0
Restricted Mean Survival Time (RMST) Ratios
Log Est. SE Est. CIL CIU p
Groups 1 vs. 0 -0.086 0.019 0.918 0.885 0.952 0Export
Show code
vector_var_names<-c("hash_key", "edad_al_ing", "fech_ing", "fech_egres_imp", "event", "diff_bet_treat", "tipo_de_programa_2", "abandono_temprano", "motivodeegreso_mod_imp")
prueba2_imp2%>%
dplyr::arrange(hash_key, edad_al_ing)%>%
#dplyr::left_join(prueba2[,vector_var_names], by=c("hash_key"="hash_key", "edad_al_ing"="edad_al_ing")) %>%
#dplyr::left_join(prueba2_imp2[,c("hash_key","edad_al_ing","duplicates_filtered_adj","person_years","max_cum_dias_trat_sin_na_adj")], by=c("hash_key"="hash_key", "edad_al_ing"="edad_al_ing")) %>%
rio::export(file = paste0(gsub("analisis_joel2.Rmd","",path),"CONS_C1_df_dup_SEP_2020_joel_feb_2023.dta"))
if(no_mostrar==1){
#23,979
missing.values2<-
prueba2 %>%
rowwise %>%
dplyr::mutate_all(~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>%
dplyr::summarise_all(~sum(.))
invisible("Para ver los nombres de las variables")
for(i in 1:length(prueba)){
print(paste0(names(prueba)[[i]],"= ",attr(prueba[[i]],"label")))
}
}
Flowchart
Show code
tab1_lab<- paste0('Original C1 Dataset \n(n = ', formatC(nrow(CONS_C1), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1%>% dplyr::distinct(HASH_KEY)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab2_lab<- paste0('C1 Dataset \n(n = ', formatC(nrow(CONS_C1_df_dup_SEP_2020), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab1_5_lab_1<- paste0('(n=', CONS_C1_df_dup_SEP_2020 %>% dplyr::group_by(hash_key) %>% dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>% dplyr::ungroup() %>% dplyr::filter(edad_al_ing<18) %>% nrow(), '; users= ',CONS_C1_df_dup_SEP_2020 %>% dplyr::group_by(hash_key) %>% dplyr::mutate(menor_edad=dplyr::case_when(edad_al_ing<18~1,TRUE~0),menor_edad=sum(menor_edad,na.rm=T)) %>% dplyr::ungroup() %>% dplyr::filter(edad_al_ing<18) %>% dplyr::distinct(hash_key)%>% nrow(), ')')
tab1_5_lab_2<- paste0('(n=', prueba%>% dplyr::filter(edad_al_ing_grupos!="18-29"|is.na(edad_al_ing_grupos)) %>% dplyr::distinct(hash_key) %>% nrow() %>% format(big.mark=","),'; users=', prueba%>% dplyr::filter(edad_al_ing_grupos!="18-29"|is.na(edad_al_ing_grupos)) %>% nrow() %>% format(big.mark=","),')')
tab1_5_lab<- paste0('• Discarded cases with no age at admission or ages below 18 years old ', tab1_5_lab_1,'\\\\\\l• Discarded ages different than between 18-29 years old at admission ',tab1_5_lab_2,'\\\\\\l')
tab4_lab<- paste0('C1 Dataset on Young Adults \n(n = ',format(nrow(prueba2), big.mark=","),')')#, formatC(nrow(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_conservados), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_conservados%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab3_5_lab<- paste0('C1 Dataset \n(n = ')#, formatC(nrow(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_descartados), format='f', big.mark=',', digits=0), ';\nusers: ',formatC(CONS_C1_df_dup_SEP_2020_match_miss_after_imp_descartados%>% dplyr::distinct(hash_key)%>% nrow(), format='f', big.mark=',', digits=0),')')
tab5_lab<- paste0('• Imputed values of users with at least 1 missing value (n= ',prueba2 %>%
rowwise %>% dplyr::mutate_at(.vars = vars(vector_variables),.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>% dplyr::select(any_of(vector_variables)) %>% dplyr::mutate(total_na=rowSums(., na.rm=T)) %>% dplyr::filter(total_na>0) %>% nrow() %>% format(big.mark=","),'; ', round(prueba2 %>%
rowwise %>% dplyr::mutate_at(.vars = vars(vector_variables),.funs = ~ifelse(is.na(.), 1, 0)) %>%
dplyr::ungroup() %>% dplyr::select(any_of(vector_variables)) %>% dplyr::mutate(total_na=rowSums(., na.rm=T)) %>% dplyr::filter(total_na>0) %>% nrow()/nrow(prueba2),2)*100 ,'%)\\\\\\l• Substance use onset age and Initial substance were the variables with more missing values and had ', round(parse_number(data.frame(miss_val_bar)[1,3]),0),'% each one \\\\\\l')
lab_tab<- paste0(" Result of the\nimputed dataset\n(n= ",format(nrow(prueba2_imp2),big.mark=","),")")#,table(table(t_id_1)) %>% formatC(big.mark = ","),"; No. of controls: ",table(table(c_id_1))%>% formatC(big.mark = ","))
#https://stackoverflow.com/questions/46750364/diagrammer-and-graphviz
#https://mikeyharper.uk/flowcharts-in-r-using-diagrammer/
#http://blog.nguyenvq.com/blog/2012/05/29/better-decision-tree-graphics-for-rpart-via-party-and-partykit/
#http://blog.nguyenvq.com/blog/2014/01/17/skeleton-to-create-fast-automatic-tree-diagrams-using-r-and-graphviz/
#https://cran.r-project.org/web/packages/DiagrammeR/vignettes/graphviz-mermaid.html
#https://stackoverflow.com/questions/39133058/how-to-use-graphviz-graphs-in-diagrammer-for-r
#https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781789802566/1/ch01lvl1sec21/creating-diagrams-via-the-diagrammer-package
#https://justlegal.be/2019/05/using-flowcharts-to-display-legal-procedures/
# paste0("No. of treatments: ",table(table(t_id_1)) %>% formatC(big.mark = ","),"; No. of controls: ",table(table(c_id_1))%>% formatC(big.mark = ","))
#
library(DiagrammeR) #⋉
flowchrt_p1<-
grViz([1468 chars quoted with '"'], width = 1200,
height = 900)
DPI = 1200
WidthCM = 11
HeightCM = 8
flowchrt_p1
Show code
flowchrt_p1 %>%
export_svg %>% charToRaw %>% rsvg_pdf("_flowchart_joel.pdf")
flowchrt_p1 %>% export_svg()%>%charToRaw %>% rsvg(width = WidthCM *(DPI/2.54), height = HeightCM *(DPI/2.54)) %>% png::writePNG("_flowchart_joel.png")
htmlwidgets::saveWidget(flowchrt_p1, "_flowchart_joel.html")
webshot::webshot("_flowchart_joel.html", "_flowchart_joel_corr.png",vwidth = 1200, vheight = 900, zoom = 3)
Session Info
Show code
Sys.getenv("R_LIBS_USER")
[1] "C:/Users/CISS Fondecyt/OneDrive/Documentos/R/win-library/4.0"Show code
rstudioapi::getSourceEditorContext()
Document Context:
- id: 'EA3D206D'
- path: 'C:/Users/CISS Fondecyt/Mi unidad/Alvacast/SISTRAT 2019 (github)/analisis_joel2.Rmd'
- contents: <2274 rows>
Document Selection:
- [2215, 14] -- [2215, 14]: ''Show code
save.image("__analisis_joel.RData")
unlink("*_cache", recursive = T, force = T, expand = TRUE)
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))